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2018 | OriginalPaper | Buchkapitel

7. Multi-objective Optimization

verfasst von : Carlos A. Coello Coello

Erschienen in: Handbook of Heuristics

Verlag: Springer International Publishing

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Abstract

This chapter provides a short overview of multi-objective optimization using metaheuristics. The chapter includes a description of some of the main metaheuristics that have been used for multi-objective optimization. Although special emphasis is made on evolutionary algorithms, other metaheuristics, such as particle swarm optimization, artificial immune systems, and ant colony optimization, are also briefly discussed. Other topics such as applications and recent algorithmic trends are also included. Finally, some of the main research trends that are worth exploring in this area are briefly discussed.

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Literatur
1.
Zurück zum Zitat Abboud K, Schoenauer M (2002) Surrogate deterministic mutation. In: Collet P, Fonlupt C, Hao J-K, Lutton E, Schoenauer M (eds) Artificial evolution, 5th international conference, evolution artificielle, EA 2001. Lecture notes in computer science, vol 2310. Springer, Le Creusot, pp 103–115 Abboud K, Schoenauer M (2002) Surrogate deterministic mutation. In: Collet P, Fonlupt C, Hao J-K, Lutton E, Schoenauer M (eds) Artificial evolution, 5th international conference, evolution artificielle, EA 2001. Lecture notes in computer science, vol 2310. Springer, Le Creusot, pp 103–115
2.
Zurück zum Zitat Akay B (2013) Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms. J Glob Optim 57(2):415–445 Akay B (2013) Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms. J Glob Optim 57(2):415–445
3.
Zurück zum Zitat Alba E, Luque G, Nesmachnow S (2013) Parallel metaheuristics: recent advances and new trends. Int Trans Oper Res 20(1):1–48 Alba E, Luque G, Nesmachnow S (2013) Parallel metaheuristics: recent advances and new trends. Int Trans Oper Res 20(1):1–48
4.
Zurück zum Zitat Angus D, Woodward C (2009) Multiple objective ant colony optimisation. Swarm Intell 3(1):69–85 Angus D, Woodward C (2009) Multiple objective ant colony optimisation. Swarm Intell 3(1):69–85
5.
Zurück zum Zitat Antonio LM, Coello Coello CA (2013) Use of cooperative coevolution for solving large scale multiobjective optimization problems. In: 2013 IEEE congress on evolutionary computation (CEC’2013), Cancún. IEEE Press, pp 2758–2765. ISBN:978-1-4799-0454-9 Antonio LM, Coello Coello CA (2013) Use of cooperative coevolution for solving large scale multiobjective optimization problems. In: 2013 IEEE congress on evolutionary computation (CEC’2013), Cancún. IEEE Press, pp 2758–2765. ISBN:978-1-4799-0454-9
6.
Zurück zum Zitat Arias-Montaño A, Coello Coello CA, Mezura-Montes E (2012) Multi-objective evolutionary algorithms in aeronautical and aerospace engineering. IEEE Trans Evol Comput 16(5): 662–694 Arias-Montaño A, Coello Coello CA, Mezura-Montes E (2012) Multi-objective evolutionary algorithms in aeronautical and aerospace engineering. IEEE Trans Evol Comput 16(5): 662–694
7.
Zurück zum Zitat Bader J, Zitzler E (2011) HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol Comput 19(1):45–76. Spring Bader J, Zitzler E (2011) HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol Comput 19(1):45–76. Spring
8.
Zurück zum Zitat Bai Q, Labi S, Sinha KC (2012) Trade-off analysis for multiobjective optimization in transportation asset management by generating Pareto frontiers using extreme points nondominated sorting genetic algorithm II. J Trans Eng-ASCE 138(6):798–808 Bai Q, Labi S, Sinha KC (2012) Trade-off analysis for multiobjective optimization in transportation asset management by generating Pareto frontiers using extreme points nondominated sorting genetic algorithm II. J Trans Eng-ASCE 138(6):798–808
9.
Zurück zum Zitat Balesdent M, Berend N, Depince P, Chriette A (2012) A survey of multidisciplinary design optimization methods in launch vehicle design. Struct Multidiscip Optim 45(5):619–642 Balesdent M, Berend N, Depince P, Chriette A (2012) A survey of multidisciplinary design optimization methods in launch vehicle design. Struct Multidiscip Optim 45(5):619–642
10.
Zurück zum Zitat Balling R, Wilson S (2001) The maximin fitness function for multi-objective evolutionary computation: application to city planning. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt H-M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO’2001), San Francisco. Morgan Kaufmann Publishers, pp 1079–1084 Balling R, Wilson S (2001) The maximin fitness function for multi-objective evolutionary computation: application to city planning. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt H-M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO’2001), San Francisco. Morgan Kaufmann Publishers, pp 1079–1084
11.
Zurück zum Zitat Banks A, Vincent J, Anyakoha C (2008) A review of particle swarm optimization. II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat Comput 7(1):109–124. Unconventional Computation 2006, Selected Papers Banks A, Vincent J, Anyakoha C (2008) A review of particle swarm optimization. II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat Comput 7(1):109–124. Unconventional Computation 2006, Selected Papers
12.
Zurück zum Zitat Baños R, Gil C, Reca J, Martínez J (2009) Implementation of scatter search for multi-objective optimization: a comparative study. Comput Optim Appl 42(3):421–441 Baños R, Gil C, Reca J, Martínez J (2009) Implementation of scatter search for multi-objective optimization: a comparative study. Comput Optim Appl 42(3):421–441
13.
Zurück zum Zitat Baronas R, Žilinskas A, Litvinas L (2016) Optimal design of amperometric biosensors applying multi-objective optimization and decision visualization. Electrochim Acta 211: 586–594 Baronas R, Žilinskas A, Litvinas L (2016) Optimal design of amperometric biosensors applying multi-objective optimization and decision visualization. Electrochim Acta 211: 586–594
14.
Zurück zum Zitat Bartolini R, Apollonio M, Martin IPS (2012) Multi-objective genetic algorithm optimization of the beam dynamics in linac drivers for free electron lasers. Phys Rev Spec Top Accel Beams 15(3). Article number:030701 Bartolini R, Apollonio M, Martin IPS (2012) Multi-objective genetic algorithm optimization of the beam dynamics in linac drivers for free electron lasers. Phys Rev Spec Top Accel Beams 15(3). Article number:030701
15.
Zurück zum Zitat Beausoleil RP (2006) “MOSS” multiobjective scatter search applied to non-linear multiple criteria optimization. Eur J Oper Res 169(2):426–449 Beausoleil RP (2006) “MOSS” multiobjective scatter search applied to non-linear multiple criteria optimization. Eur J Oper Res 169(2):426–449
16.
Zurück zum Zitat Beausoleil RP (2008) “MOSS-II” Tabu/Scatter search for nonlinear multiobjective optimization. In: Siarry P, Michalewicz Z (eds) Advances in metaheuristic methods for hard optimization. Springer, Berlin, pp 39–67. ISBN:978-3-540-72959-4 Beausoleil RP (2008) “MOSS-II” Tabu/Scatter search for nonlinear multiobjective optimization. In: Siarry P, Michalewicz Z (eds) Advances in metaheuristic methods for hard optimization. Springer, Berlin, pp 39–67. ISBN:978-3-540-72959-4
17.
Zurück zum Zitat Benyoucef L, Xie X (2011) Supply chain design using simulation-based NSGA-II approach. In: Wang L, Ng AHC, Deb K (eds) Multi-objective evolutionary optimisation for product design and manufacturing. Springer, London, pp 455–491. ISBN:978-0-85729-617-7. Chapter 17 Benyoucef L, Xie X (2011) Supply chain design using simulation-based NSGA-II approach. In: Wang L, Ng AHC, Deb K (eds) Multi-objective evolutionary optimisation for product design and manufacturing. Springer, London, pp 455–491. ISBN:978-0-85729-617-7. Chapter 17
18.
Zurück zum Zitat Bernardes de Oliveira F, Davendra D, Gadelha Guimar aes F (2013) Multi-objective differential evolution on the GPU with C-CUDA. In: Snášel V, Abraham A, Corchado ES (eds) Soft computing models in industrial and environmental applications, 7th international conference (SOCO’12). Advances in intelligent systems and computing, vol 188. Springer, Ostrava, pp 123–132 Bernardes de Oliveira F, Davendra D, Gadelha Guimar aes F (2013) Multi-objective differential evolution on the GPU with C-CUDA. In: Snášel V, Abraham A, Corchado ES (eds) Soft computing models in industrial and environmental applications, 7th international conference (SOCO’12). Advances in intelligent systems and computing, vol 188. Springer, Ostrava, pp 123–132
19.
Zurück zum Zitat Beume N, Naujoks B, Emmerich M (2007) SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur J Oper Res 181(3):1653–1669 Beume N, Naujoks B, Emmerich M (2007) SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur J Oper Res 181(3):1653–1669
20.
Zurück zum Zitat Bhattacharya M, Lu G (2003) A dynamic approximate fitness based hybrid ea for optimization problems. In: Proceedings of IEEE congress on evolutionary computation, pp 1879–1886 Bhattacharya M, Lu G (2003) A dynamic approximate fitness based hybrid ea for optimization problems. In: Proceedings of IEEE congress on evolutionary computation, pp 1879–1886
21.
Zurück zum Zitat Branke J (2002) Evolutionary optimization in dynamic environments. Kluwer Academic Publishers, Boston. ISBN:0-7923-7631-5 Branke J (2002) Evolutionary optimization in dynamic environments. Kluwer Academic Publishers, Boston. ISBN:0-7923-7631-5
22.
Zurück zum Zitat Branke J (2008) Consideration of partial user preferences in evolutionary multiobjective optimization. In: Branke J, Deb K, Miettinen K, Slowinski R (eds) Multiobjective optimization. Interactive and evolutionary approaches. Lecture notes in computer science, vol 5252. Springer, Berlin, pp 157–178 Branke J (2008) Consideration of partial user preferences in evolutionary multiobjective optimization. In: Branke J, Deb K, Miettinen K, Slowinski R (eds) Multiobjective optimization. Interactive and evolutionary approaches. Lecture notes in computer science, vol 5252. Springer, Berlin, pp 157–178
23.
Zurück zum Zitat Brockhoff D, Friedrich T, Hebbinghaus N, Klein C, Neumann F, Zitzler E (2007) Do additional objectives make a problem harder? In: Thierens D (ed) 2007 genetic and evolutionary computation conference (GECCO’2007), vol 1. ACM Press, London, pp 765–772 Brockhoff D, Friedrich T, Hebbinghaus N, Klein C, Neumann F, Zitzler E (2007) Do additional objectives make a problem harder? In: Thierens D (ed) 2007 genetic and evolutionary computation conference (GECCO’2007), vol 1. ACM Press, London, pp 765–772
24.
Zurück zum Zitat Brockhoff D, Wagner T, Trautmann H (2012) On the properties of the R2 indicator. In: 2012 genetic and evolutionary computation conference (GECCO’2012). ACM Press, Philadelphia, pp 465–472. ISBN:978-1-4503-1177-9 Brockhoff D, Wagner T, Trautmann H (2012) On the properties of the R2 indicator. In: 2012 genetic and evolutionary computation conference (GECCO’2012). ACM Press, Philadelphia, pp 465–472. ISBN:978-1-4503-1177-9
25.
Zurück zum Zitat Bueche D, Schraudolph NN, Koumoutsakos P (2005) Accelerating evolutionary algorithms with gaussian process fitness function models. IEEE Trans Syst Man Cybern Part C 35(2):183–194 Bueche D, Schraudolph NN, Koumoutsakos P (2005) Accelerating evolutionary algorithms with gaussian process fitness function models. IEEE Trans Syst Man Cybern Part C 35(2):183–194
26.
Zurück zum Zitat Burke EK, Li J, Qu R (2012) A Pareto-based search methodology for multi-objective nurse scheduling. Ann Oper Res 196(1):91–109 Burke EK, Li J, Qu R (2012) A Pareto-based search methodology for multi-objective nurse scheduling. Ann Oper Res 196(1):91–109
27.
Zurück zum Zitat Campelo F, Guimar aes FG, Saldanha RR, Igarashi H, Noguchi S, Lowther DA, Ramirez JA (2004) A novel multiobjective immune algorithm using nondominated sorting. In: 11th international IGTE symposium on numerical field calculation in electrical engineering, Seggauberg Campelo F, Guimar aes FG, Saldanha RR, Igarashi H, Noguchi S, Lowther DA, Ramirez JA (2004) A novel multiobjective immune algorithm using nondominated sorting. In: 11th international IGTE symposium on numerical field calculation in electrical engineering, Seggauberg
28.
Zurück zum Zitat Campelo F, Guimar aes FG, Igarashi H (2007) Overview of artificial immune systems for multi-objective optimization. In: Obayashi S, Deb K, Poloni C, Hiroyasu T, Murata T (eds) Evolutionary multi-criterion optimization, 4th international conference (EMO 2007), Matshushima. Lecture notes in computer science, vol 4403. Springer, pp 937–951 Campelo F, Guimar aes FG, Igarashi H (2007) Overview of artificial immune systems for multi-objective optimization. In: Obayashi S, Deb K, Poloni C, Hiroyasu T, Murata T (eds) Evolutionary multi-criterion optimization, 4th international conference (EMO 2007), Matshushima. Lecture notes in computer science, vol 4403. Springer, pp 937–951
29.
Zurück zum Zitat Campos SC, Arroyo JEC (2014) NSGA-II with iterated greedy for a bi-objective three-stage assembly flowshop scheduling problem. In: 2014 genetic and evolutionary computation conference (GECCO 2014), Vancouver. ACM Press, pp 429–436. ISBN:978-1-4503-2662-9 Campos SC, Arroyo JEC (2014) NSGA-II with iterated greedy for a bi-objective three-stage assembly flowshop scheduling problem. In: 2014 genetic and evolutionary computation conference (GECCO 2014), Vancouver. ACM Press, pp 429–436. ISBN:978-1-4503-2662-9
30.
Zurück zum Zitat Carcangiu S, Fanni A, Montisci A (2008) Multiobjective Tabu search algorithms for optimal design of electromagnetic devices. IEEE Trans Magn 44(6):970–973 Carcangiu S, Fanni A, Montisci A (2008) Multiobjective Tabu search algorithms for optimal design of electromagnetic devices. IEEE Trans Magn 44(6):970–973
31.
Zurück zum Zitat Carrese R, Winarto H, Li X, Sobester A, Ebenezer S (2012) A comprehensive preference-based optimization framework with application to high-lift aerodynamic design. Eng Optim 44(10):1209–1227 Carrese R, Winarto H, Li X, Sobester A, Ebenezer S (2012) A comprehensive preference-based optimization framework with application to high-lift aerodynamic design. Eng Optim 44(10):1209–1227
32.
Zurück zum Zitat Chang Y-C (2012) Multi-objective optimal SVC installation for power system loading margin improvement. IEEE Trans Power Syst 27(2):984–992 Chang Y-C (2012) Multi-objective optimal SVC installation for power system loading margin improvement. IEEE Trans Power Syst 27(2):984–992
33.
Zurück zum Zitat Chaves-Gonzalez JM, Vega-Rodriguez MA, Granado-Criado JM (2013) A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design. Eng Appl Artif Intel 26(9):2045–2057 Chaves-Gonzalez JM, Vega-Rodriguez MA, Granado-Criado JM (2013) A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design. Eng Appl Artif Intel 26(9):2045–2057
34.
Zurück zum Zitat Chikumbo O, Goodman E, Deb K (2012) Approximating a multi-dimensional Pareto front for a land use management problem: a modified MOEA with an epigenetic silencing metaphor. In: 2012 IEEE congress on evolutionary computation (CEC’2012), Brisbane. IEEE Press, pp 480–488 Chikumbo O, Goodman E, Deb K (2012) Approximating a multi-dimensional Pareto front for a land use management problem: a modified MOEA with an epigenetic silencing metaphor. In: 2012 IEEE congress on evolutionary computation (CEC’2012), Brisbane. IEEE Press, pp 480–488
35.
Zurück zum Zitat Coello Coello CA (2000) Constraint-handling using an evolutionary multiobjective optimization technique. Civ Eng Environ Syst 17:319–346 Coello Coello CA (2000) Constraint-handling using an evolutionary multiobjective optimization technique. Civ Eng Environ Syst 17:319–346
36.
Zurück zum Zitat Coello Coello CA (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287 Coello Coello CA (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287
37.
Zurück zum Zitat Coello Coello CA (2011) An introduction to multi-objective particle swarm optimizers. In: Gaspar-Cunha A, Takahashi R, Schaefer G, Costa L (eds) Soft computing in industrial applications. Advances in intelligent and soft computing series, vol 96. Springer, Berlin, pp 3–12. ISBN:978-3-642-20504-0 Coello Coello CA (2011) An introduction to multi-objective particle swarm optimizers. In: Gaspar-Cunha A, Takahashi R, Schaefer G, Costa L (eds) Soft computing in industrial applications. Advances in intelligent and soft computing series, vol 96. Springer, Berlin, pp 3–12. ISBN:978-3-642-20504-0
38.
Zurück zum Zitat Coello Coello CA, Cruz Cortés N (2005) Solving multiobjective optimization problems using an artificial immune system. Genet Program Evolvable Mach 6(2):163–190 Coello Coello CA, Cruz Cortés N (2005) Solving multiobjective optimization problems using an artificial immune system. Genet Program Evolvable Mach 6(2):163–190
39.
Zurück zum Zitat Coello Coello CA, Toscano Pulido G (2001) Multiobjective optimization using a micro-genetic algorithm. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt H-M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO’2001), San Francisco. Morgan Kaufmann Publishers, pp 274–282 Coello Coello CA, Toscano Pulido G (2001) Multiobjective optimization using a micro-genetic algorithm. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt H-M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO’2001), San Francisco. Morgan Kaufmann Publishers, pp 274–282
40.
Zurück zum Zitat Coello Coello CA, Toscano Pulido G, Salazar Lechuga M (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279 Coello Coello CA, Toscano Pulido G, Salazar Lechuga M (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279
41.
Zurück zum Zitat Coello Coello CA, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Springer, New York. ISBN:978-0-387-33254-3 Coello Coello CA, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Springer, New York. ISBN:978-0-387-33254-3
42.
Zurück zum Zitat Collette Y, Siarry P (2003) Multiobjective optimization. Principles and case studies. Springer Berlin, Germany. ISBN:3-540-40182-2 Collette Y, Siarry P (2003) Multiobjective optimization. Principles and case studies. Springer Berlin, Germany. ISBN:3-540-40182-2
43.
Zurück zum Zitat Corne D, Glover F, Dorigo M (eds) (1999) New ideas in optimization. McGraw-Hill, Berkshire. ISBN:007-709506-5 Corne D, Glover F, Dorigo M (eds) (1999) New ideas in optimization. McGraw-Hill, Berkshire. ISBN:007-709506-5
44.
Zurück zum Zitat Corne DW, Knowles JD, Oates MJ (2000) The Pareto envelope-based selection algorithm for multiobjective optimization. In: Schoenauer M, Deb K, Rudolph G, Yao X, Lutton E, Merelo JJ, Schwefel H-P (eds) Proceedings of the parallel problem solving from nature VI conference, Paris. Lecture notes in computer science, vol 1917. Springer, pp 839–848 Corne DW, Knowles JD, Oates MJ (2000) The Pareto envelope-based selection algorithm for multiobjective optimization. In: Schoenauer M, Deb K, Rudolph G, Yao X, Lutton E, Merelo JJ, Schwefel H-P (eds) Proceedings of the parallel problem solving from nature VI conference, Paris. Lecture notes in computer science, vol 1917. Springer, pp 839–848
45.
Zurück zum Zitat Corne DW, Jerram NR, Knowles JD, Oates MJ (2001) PESA-II: region-based selection in evolutionary multiobjective optimization. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt H-M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO’2001), San Francisco. Morgan Kaufmann Publishers, pp 283–290 Corne DW, Jerram NR, Knowles JD, Oates MJ (2001) PESA-II: region-based selection in evolutionary multiobjective optimization. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt H-M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO’2001), San Francisco. Morgan Kaufmann Publishers, pp 283–290
46.
Zurück zum Zitat Cui X, Li M, Fang T (2001) Study of population diversity of multiobjective evolutionary algorithm based on immune and entropy principles. In: Proceedings of the congress on evolutionary computation 2001 (CEC’2001), Piscataway, vol 2. IEEE Service Center, pp 1316–1321 Cui X, Li M, Fang T (2001) Study of population diversity of multiobjective evolutionary algorithm based on immune and entropy principles. In: Proceedings of the congress on evolutionary computation 2001 (CEC’2001), Piscataway, vol 2. IEEE Service Center, pp 1316–1321
47.
Zurück zum Zitat Cvetković D, Parmee IC (2002) Preferences and their application in evolutionary multiobjective optimisation. IEEE Trans Evol Comput 6(1):42–57 Cvetković D, Parmee IC (2002) Preferences and their application in evolutionary multiobjective optimisation. IEEE Trans Evol Comput 6(1):42–57
48.
Zurück zum Zitat Czyzak P, Jaszkiewicz A (1998) Pareto simulated annealing—a metaheuristic technique for multiple-objective combinatorial optimization. J Multi-Criteria Decis Anal 7:34–47 Czyzak P, Jaszkiewicz A (1998) Pareto simulated annealing—a metaheuristic technique for multiple-objective combinatorial optimization. J Multi-Criteria Decis Anal 7:34–47
49.
Zurück zum Zitat Das I, Dennis J (1997) A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Struct Optim 14(1):63–69 Das I, Dennis J (1997) A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Struct Optim 14(1):63–69
50.
Zurück zum Zitat Dasgupta D (ed) (1999) Artificial immune systems and their applications. Springer, Berlin Dasgupta D (ed) (1999) Artificial immune systems and their applications. Springer, Berlin
51.
Zurück zum Zitat de Castro LN, Timmis J (2002) An introduction to artificial immune systems: a new computational intelligence paradigm. Springer, London. ISBN:1-85233-594-7 de Castro LN, Timmis J (2002) An introduction to artificial immune systems: a new computational intelligence paradigm. Springer, London. ISBN:1-85233-594-7
52.
Zurück zum Zitat Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester. ISBN:0-471-87339-X Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester. ISBN:0-471-87339-X
53.
Zurück zum Zitat Deb K, Goldberg DE (1989) An investigation of niche and species formation in genetic function optimization. In: Schaffer JD (ed) Proceedings of the third international conference on genetic algorithms, San Mateo. George Mason University, Morgan Kaufmann Publishers, pp 42–50 Deb K, Goldberg DE (1989) An investigation of niche and species formation in genetic function optimization. In: Schaffer JD (ed) Proceedings of the third international conference on genetic algorithms, San Mateo. George Mason University, Morgan Kaufmann Publishers, pp 42–50
54.
Zurück zum Zitat Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans Evol Comput 18(4):577–601 Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans Evol Comput 18(4):577–601
55.
Zurück zum Zitat Deb K, Pratap A, Meyarivan T (2001) Constrained test problems for multi-objective evolutionary optimization. In: Zitzler E, Deb K, Thiele L, Coello Coello CA, Corne D (eds) First international conference on evolutionary multi-criterion optimization. Lecture notes in computer science, vol 1993. Springer, pp 284–298 Deb K, Pratap A, Meyarivan T (2001) Constrained test problems for multi-objective evolutionary optimization. In: Zitzler E, Deb K, Thiele L, Coello Coello CA, Corne D (eds) First international conference on evolutionary multi-criterion optimization. Lecture notes in computer science, vol 1993. Springer, pp 284–298
56.
Zurück zum Zitat Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA–II. IEEE Trans Evol Comput 6(2):182–197 Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA–II. IEEE Trans Evol Comput 6(2):182–197
57.
Zurück zum Zitat Deb K, Mohan M, Mishra S (2005) Evaluating the 𝜖-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evol Comput 13(4):501–525. Winter Deb K, Mohan M, Mishra S (2005) Evaluating the 𝜖-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evol Comput 13(4):501–525. Winter
58.
Zurück zum Zitat Dhouib S, Dhouib S, Chabchoub H (2013) Artificial bee colony metaheuristic to find Pareto optimal solutions set for engineering design problems. In: 2013 5th international conference on modeling, simulation and applied optimization (ICMSAO), Hammamet. IEEE Press. ISBN:978-1-4673-5812-5 Dhouib S, Dhouib S, Chabchoub H (2013) Artificial bee colony metaheuristic to find Pareto optimal solutions set for engineering design problems. In: 2013 5th international conference on modeling, simulation and applied optimization (ICMSAO), Hammamet. IEEE Press. ISBN:978-1-4673-5812-5
59.
Zurück zum Zitat di Pierro F, Khu S-T, Savić DA (2007) An investigation on preference order ranking scheme for multiobjective evolutionary optimization. IEEE Trans Evol Comput 11(1): 17–45 di Pierro F, Khu S-T, Savić DA (2007) An investigation on preference order ranking scheme for multiobjective evolutionary optimization. IEEE Trans Evol Comput 11(1): 17–45
60.
Zurück zum Zitat Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, Cambridge. ISBN:0-262-04219-3 Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, Cambridge. ISBN:0-262-04219-3
61.
Zurück zum Zitat Durillo JJ, García-Nieto J, Nebro AJ, Coello Coello CA, Luna F, Alba E (2009) Multi-objective particle swarm optimizers: an experimental comparison. In: Ehrgott M, Fonseca CM, Gandibleux X, Hao J-K, Sevaux M (eds) Evolutionary multi-criterion optimization. 5th international conference (EMO 2009). Lecture notes in computer science, vol 5467. Springer, Nantes, pp 495–509 Durillo JJ, García-Nieto J, Nebro AJ, Coello Coello CA, Luna F, Alba E (2009) Multi-objective particle swarm optimizers: an experimental comparison. In: Ehrgott M, Fonseca CM, Gandibleux X, Hao J-K, Sevaux M (eds) Evolutionary multi-criterion optimization. 5th international conference (EMO 2009). Lecture notes in computer science, vol 5467. Springer, Nantes, pp 495–509
62.
Zurück zum Zitat Durillo JJ, Nebro AJ, Coello Coello CA, Garcia-Nieto J, Luna F, Alba E (2010) A study of multiobjective metaheuristics when solving parameter scalable problems. IEEE Trans Evol Comput 14(4):618–635 Durillo JJ, Nebro AJ, Coello Coello CA, Garcia-Nieto J, Luna F, Alba E (2010) A study of multiobjective metaheuristics when solving parameter scalable problems. IEEE Trans Evol Comput 14(4):618–635
63.
Zurück zum Zitat Edgeworth FY (1881) Mathematical psychics. P. Keagan, London Edgeworth FY (1881) Mathematical psychics. P. Keagan, London
64.
Zurück zum Zitat Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin. ISBN:3-540-40184-9 Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin. ISBN:3-540-40184-9
65.
Zurück zum Zitat Ekbal A, Saha S (2013) Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition. Soft Comput 17(1):1–16 Ekbal A, Saha S (2013) Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition. Soft Comput 17(1):1–16
66.
Zurück zum Zitat Emmerich M, Giotis A, Özdemir M, Bäck T, Giannakoglou K (2002) Metamodel-assisted evolution strategies. In: Merelo Guervós JJ, Adamidis P, Beyer H-G, Fernández-Villaca nas J-L, Schwefel H-P (eds) Parallel problem solving from nature—PPSN VII, Granada. Lecture notes in computer science, vol 2439. Springer, pp 371–380 Emmerich M, Giotis A, Özdemir M, Bäck T, Giannakoglou K (2002) Metamodel-assisted evolution strategies. In: Merelo Guervós JJ, Adamidis P, Beyer H-G, Fernández-Villaca nas J-L, Schwefel H-P (eds) Parallel problem solving from nature—PPSN VII, Granada. Lecture notes in computer science, vol 2439. Springer, pp 371–380
67.
Zurück zum Zitat Emmerich M, Beume N, Naujoks B (2005) An EMO algorithm using the hypervolume measure as selection criterion. In: Coello Coello CA, Hernández Aguirre A, Zitzler E (eds) Evolutionary multi-criterion optimization. Third international conference (EMO 2005), Guanajuato. Lecture notes in computer science, vol 3410. Springer, pp 62–76 Emmerich M, Beume N, Naujoks B (2005) An EMO algorithm using the hypervolume measure as selection criterion. In: Coello Coello CA, Hernández Aguirre A, Zitzler E (eds) Evolutionary multi-criterion optimization. Third international conference (EMO 2005), Guanajuato. Lecture notes in computer science, vol 3410. Springer, pp 62–76
68.
Zurück zum Zitat Eppe S, López-Ibá nez M, Stützle T, De Smet Y (2011) An experimental study of preference model integration into multi-objective optimization heuristics. In: 2011 IEEE congress on evolutionary computation (CEC’2011), New Orleans. IEEE Service Center, pp 2751–2758 Eppe S, López-Ibá nez M, Stützle T, De Smet Y (2011) An experimental study of preference model integration into multi-objective optimization heuristics. In: 2011 IEEE congress on evolutionary computation (CEC’2011), New Orleans. IEEE Service Center, pp 2751–2758
69.
Zurück zum Zitat Esparcia-Alcazar AI, Martínez-García A, García-Sánchez P, Merelo JJ, Mora AM (2013) Towards a multiobjective evolutionary approach to inventory and routing management in a retail chain. In: 2013 IEEE congress on evolutionary computation (CEC’2013), Cancún. IEEE Press, pp 3166–3173. ISBN:978-1-4799-0454-9 Esparcia-Alcazar AI, Martínez-García A, García-Sánchez P, Merelo JJ, Mora AM (2013) Towards a multiobjective evolutionary approach to inventory and routing management in a retail chain. In: 2013 IEEE congress on evolutionary computation (CEC’2013), Cancún. IEEE Press, pp 3166–3173. ISBN:978-1-4799-0454-9
70.
Zurück zum Zitat Falcon-Cardona JG, Coello Coello CA (2017) A new indicator-based many-objective ant colony optimizer for continuous search spaces. Swarm Intell 11(1):71–100 Falcon-Cardona JG, Coello Coello CA (2017) A new indicator-based many-objective ant colony optimizer for continuous search spaces. Swarm Intell 11(1):71–100
71.
Zurück zum Zitat Fang G, Xue M, Su M, Hu D, Li Y, Xiong B, Ma L, Meng T, Chen Y, Li J, Li J, Shen J (2012) CCLab-a multi-objective genetic algorithm based combinatorial library desing software and an application for histone deacetylase inhibitor desing. Bioorg Med Chem Lett 22(14): 4540–4545 Fang G, Xue M, Su M, Hu D, Li Y, Xiong B, Ma L, Meng T, Chen Y, Li J, Li J, Shen J (2012) CCLab-a multi-objective genetic algorithm based combinatorial library desing software and an application for histone deacetylase inhibitor desing. Bioorg Med Chem Lett 22(14): 4540–4545
72.
Zurück zum Zitat Farina M, Amato P (2004) A fuzzy definition of “optimality” for many-criteria optimization problems. IEEE Trans Syst Man and Cybern Part A Syst Hum 34(3):315–326 Farina M, Amato P (2004) A fuzzy definition of “optimality” for many-criteria optimization problems. IEEE Trans Syst Man and Cybern Part A Syst Hum 34(3):315–326
73.
Zurück zum Zitat Fleischer M (2003) The measure of Pareto optima. Applications to multi-objective metaheuristics. In: Fonseca CM, Fleming PJ, Zitzler E, Deb K, Thiele L (eds) Evolutionary multi-criterion optimization. Second international conference (EMO 2003), Faro. Lecture notes in computer science, vol 2632. Springer, pp 519–533 Fleischer M (2003) The measure of Pareto optima. Applications to multi-objective metaheuristics. In: Fonseca CM, Fleming PJ, Zitzler E, Deb K, Thiele L (eds) Evolutionary multi-criterion optimization. Second international conference (EMO 2003), Faro. Lecture notes in computer science, vol 2632. Springer, pp 519–533
74.
Zurück zum Zitat Fogel LJ (1966) Artificial intelligence through simulated evolution. John Wiley, New York Fogel LJ (1966) Artificial intelligence through simulated evolution. John Wiley, New York
75.
Zurück zum Zitat Fogel DB (1995) Evolutionary computation. Toward a new philosophy of machine intelligence. The Institute of Electrical and Electronic Engineers, New York Fogel DB (1995) Evolutionary computation. Toward a new philosophy of machine intelligence. The Institute of Electrical and Electronic Engineers, New York
76.
Zurück zum Zitat Fogel LJ (1999) Artificial intelligence through simulated evolution. Forty years of evolutionary programming. Wiley, New York Fogel LJ (1999) Artificial intelligence through simulated evolution. Forty years of evolutionary programming. Wiley, New York
77.
Zurück zum Zitat Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Forrest S (ed) Proceedings of the fifth international conference on genetic algorithms, San Mateo. University of Illinois at Urbana-Champaign, Morgan Kauffman Publishers, pp 416–423 Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Forrest S (ed) Proceedings of the fifth international conference on genetic algorithms, San Mateo. University of Illinois at Urbana-Champaign, Morgan Kauffman Publishers, pp 416–423
78.
Zurück zum Zitat Forrest S, Perelson AS (1991) Genetic algorithms and the immune system. In: Schwefel H-P, Männer R (eds) Parallel problem solving from nature. Lecture notes in computer science. Springer, Berlin, pp 320–325 Forrest S, Perelson AS (1991) Genetic algorithms and the immune system. In: Schwefel H-P, Männer R (eds) Parallel problem solving from nature. Lecture notes in computer science. Springer, Berlin, pp 320–325
79.
Zurück zum Zitat Freschi F, Repetto M (2006) VIS: an artificial immune network for multi-objective optimization. Eng Optim 38(8):975–996 Freschi F, Repetto M (2006) VIS: an artificial immune network for multi-objective optimization. Eng Optim 38(8):975–996
80.
Zurück zum Zitat Freschi F, Coello Coello CA, Repetto M (2009) Multiobjective optimization and artificial immune systems: a review. In: Mo H (ed) Handbook of research on artificial immune systems and natural computing: applying complex adaptive technologies. Medical Information Science Reference, Hershey/New York, pp 1–21. ISBN:978-1-60566-310-4 Freschi F, Coello Coello CA, Repetto M (2009) Multiobjective optimization and artificial immune systems: a review. In: Mo H (ed) Handbook of research on artificial immune systems and natural computing: applying complex adaptive technologies. Medical Information Science Reference, Hershey/New York, pp 1–21. ISBN:978-1-60566-310-4
81.
Zurück zum Zitat Friedrich T, Kroeger T, Neumann F (2011) Weighted preferences in evolutionary multi-objective optimization. In: Wang D, Reynolds M (eds) AI 2011: advances in artificial intelligence, 24th Australasian joint conference, Perth. Lecture notes in computer science, vol 7106. Springer, pp 291–300 Friedrich T, Kroeger T, Neumann F (2011) Weighted preferences in evolutionary multi-objective optimization. In: Wang D, Reynolds M (eds) AI 2011: advances in artificial intelligence, 24th Australasian joint conference, Perth. Lecture notes in computer science, vol 7106. Springer, pp 291–300
82.
Zurück zum Zitat García-Martínez C, Cordón O, Herrera F (2007) A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. Eur J Oper Res 180(1):116–148 García-Martínez C, Cordón O, Herrera F (2007) A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. Eur J Oper Res 180(1):116–148
83.
Zurück zum Zitat Garza Fabre M, Toscano Pulido G, Coello Coello CA (2009) Ranking methods for many-objective problems. In: Aguirre AH, Borja RM, García CAR (eds) MICAI 2009: advances in artificial intelligence. 8th Mexican international conference on artificial intelligence, Guanajuato. Lecture notes in artificial intelligence, vol 5845. Springer, pp 633–645 Garza Fabre M, Toscano Pulido G, Coello Coello CA (2009) Ranking methods for many-objective problems. In: Aguirre AH, Borja RM, García CAR (eds) MICAI 2009: advances in artificial intelligence. 8th Mexican international conference on artificial intelligence, Guanajuato. Lecture notes in artificial intelligence, vol 5845. Springer, pp 633–645
84.
Zurück zum Zitat Garza-Fabre M, Rodriguez-Tello E, Toscano-Pulido G (2015) Constraint-handling through multi-objective optimization: the hydrophobic-polar model for protein structure prediction. Comput Oper Res 53:128–153 Garza-Fabre M, Rodriguez-Tello E, Toscano-Pulido G (2015) Constraint-handling through multi-objective optimization: the hydrophobic-polar model for protein structure prediction. Comput Oper Res 53:128–153
85.
Zurück zum Zitat Gen M, Cheng R (2000) Genetic algorithms and engineering optimization. Wiley series in engineering design and automation. Wiley, New York Gen M, Cheng R (2000) Genetic algorithms and engineering optimization. Wiley series in engineering design and automation. Wiley, New York
86.
Zurück zum Zitat Ghisu T, Parks GT, Jaeggi DM, Jarrett JP, Clarkson PJ (2010) The benefits of adaptive parametrization in multi-objective Tabu search optimization. Eng Optim 42(10):959–981 Ghisu T, Parks GT, Jaeggi DM, Jarrett JP, Clarkson PJ (2010) The benefits of adaptive parametrization in multi-objective Tabu search optimization. Eng Optim 42(10):959–981
87.
Zurück zum Zitat Giel O (2003) Expected runtimes of a simple multi-objective evolutionary algorithm. In: Proceedings of the 2003 congress on evolutionary computation (CEC’2003), vol 3, Canberra. IEEE Press, pp 1918–1925 Giel O (2003) Expected runtimes of a simple multi-objective evolutionary algorithm. In: Proceedings of the 2003 congress on evolutionary computation (CEC’2003), vol 3, Canberra. IEEE Press, pp 1918–1925
88.
Zurück zum Zitat Glover F, Kochenberger GA (eds) (2003) Handbook of metaheuristics. Kluwer Academic Publishers, Boston. ISBN:1-4020-7263-5 Glover F, Kochenberger GA (eds) (2003) Handbook of metaheuristics. Kluwer Academic Publishers, Boston. ISBN:1-4020-7263-5
89.
Zurück zum Zitat Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Reading Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Reading
90.
Zurück zum Zitat Goldberg DE, Deb K (1991) A comparison of selection schemes used in genetic algorithms. In: Rawlins GJE (ed) Foundations of genetic algorithms. Morgan Kaufmann, San Mateo, pp 69–93 Goldberg DE, Deb K (1991) A comparison of selection schemes used in genetic algorithms. In: Rawlins GJE (ed) Foundations of genetic algorithms. Morgan Kaufmann, San Mateo, pp 69–93
91.
Zurück zum Zitat Goldberg DE, Richardson J (1987) Genetic algorithm with sharing for multimodal function optimization. In: Grefenstette JJ (ed) Genetic algorithms and their applications: proceedings of the second international conference on genetic algorithms, Hillsdale. Lawrence Erlbaum, pp 41–49 Goldberg DE, Richardson J (1987) Genetic algorithm with sharing for multimodal function optimization. In: Grefenstette JJ (ed) Genetic algorithms and their applications: proceedings of the second international conference on genetic algorithms, Hillsdale. Lawrence Erlbaum, pp 41–49
92.
Zurück zum Zitat Gupta H, Deb K (2005) Handling constraints in robust multi-objective optimization. In: 2005 IEEE congress on evolutionary computation (CEC’2005), vol 1, Edinburgh. IEEE Service Center, pp 25–32 Gupta H, Deb K (2005) Handling constraints in robust multi-objective optimization. In: 2005 IEEE congress on evolutionary computation (CEC’2005), vol 1, Edinburgh. IEEE Service Center, pp 25–32
93.
Zurück zum Zitat Hajela P, Lin CY (1992) Genetic search strategies in multicriterion optimal design. Struct Optim 4:99–107 Hajela P, Lin CY (1992) Genetic search strategies in multicriterion optimal design. Struct Optim 4:99–107
94.
Zurück zum Zitat Hansen MP (1998) Metaheuristics for multiple objective combinatorial optimization. PhD thesis, Institute of Mathematical Modelling, Technical University of Denmark Hansen MP (1998) Metaheuristics for multiple objective combinatorial optimization. PhD thesis, Institute of Mathematical Modelling, Technical University of Denmark
95.
Zurück zum Zitat Hansen MP (2000) Tabu search for multiobjective combinatorial optimization: TAMOCO. Control Cybern 29(3):799–818 Hansen MP (2000) Tabu search for multiobjective combinatorial optimization: TAMOCO. Control Cybern 29(3):799–818
96.
Zurück zum Zitat Harada K, Sakuma J, Ono I, Kobayashi S (2007) Constraint-handling method for multi-objective function optimization: Pareto descent repair operator. In: Obayashi S, Deb K, Poloni C, Hiroyasu T, Murata T (eds) Evolutionary multi-criterion optimization, 4th international conference (EMO 2007), Matshushima. Lecture notes in computer science, vol 4403. Springer, pp 156–170 Harada K, Sakuma J, Ono I, Kobayashi S (2007) Constraint-handling method for multi-objective function optimization: Pareto descent repair operator. In: Obayashi S, Deb K, Poloni C, Hiroyasu T, Murata T (eds) Evolutionary multi-criterion optimization, 4th international conference (EMO 2007), Matshushima. Lecture notes in computer science, vol 4403. Springer, pp 156–170
97.
Zurück zum Zitat Heris SMK, Khaloozadeh H (2011) Open- and closed-loop multiobjective optimal strategies for HIV therapy using NSGA-II. IEEE Trans Biomed Eng 58(6):1678–1685 Heris SMK, Khaloozadeh H (2011) Open- and closed-loop multiobjective optimal strategies for HIV therapy using NSGA-II. IEEE Trans Biomed Eng 58(6):1678–1685
98.
Zurück zum Zitat Hernández Aguirre A, Botello Rionda S, Lizárraga Lizárraga G, Coello Coello C (2004) IS-PAES: multiobjective optimization with efficient constraint handling. In: Burczyński T, Osyczka A (eds) IUTAM symposium on evolutionary methods in mechanics. Kluwer Academic Publishers, Dordrecht/Boston/London, pp 111–120. ISBN:1-4020-2266-2 Hernández Aguirre A, Botello Rionda S, Lizárraga Lizárraga G, Coello Coello C (2004) IS-PAES: multiobjective optimization with efficient constraint handling. In: Burczyński T, Osyczka A (eds) IUTAM symposium on evolutionary methods in mechanics. Kluwer Academic Publishers, Dordrecht/Boston/London, pp 111–120. ISBN:1-4020-2266-2
99.
Zurück zum Zitat Hernández Gómez R, Coello Coello CA (2013) MOMBI: a new metaheuristic for many-objective optimization based on the R2 indicator. In: 2013 IEEE congress on evolutionary computation (CEC’2013), Cancún. IEEE Press, pp 2488–2495. ISBN:978-1-4799-0454-9 Hernández Gómez R, Coello Coello CA (2013) MOMBI: a new metaheuristic for many-objective optimization based on the R2 indicator. In: 2013 IEEE congress on evolutionary computation (CEC’2013), Cancún. IEEE Press, pp 2488–2495. ISBN:978-1-4799-0454-9
100.
Zurück zum Zitat Hernández Gómez R, Coello Coello CA, Alba Torres E (2016) A multi-objective evolutionary algorithm based on parallel coordinates. In: 2016 genetic and evolutionary computation conference (GECCO’2016), Denver. ACM Press, pp 565–572. ISBN:978-1-4503-4206-3 Hernández Gómez R, Coello Coello CA, Alba Torres E (2016) A multi-objective evolutionary algorithm based on parallel coordinates. In: 2016 genetic and evolutionary computation conference (GECCO’2016), Denver. ACM Press, pp 565–572. ISBN:978-1-4503-4206-3
101.
Zurück zum Zitat Holland JH (1962) Concerning efficient adaptive systems. In: Yovits MC, Jacobi GT, Goldstein GD (eds) Self-organizing systems—1962. Spartan Books, Washington, DC, pp 215–230 Holland JH (1962) Concerning efficient adaptive systems. In: Yovits MC, Jacobi GT, Goldstein GD (eds) Self-organizing systems—1962. Spartan Books, Washington, DC, pp 215–230
102.
Zurück zum Zitat Hong Y-S, Lee H, Tahk M-J (2003) Acceleration of the convergence speed of evolutionary algorithms using multi-layer neural networks. Eng Optim 35(1):91–102 Hong Y-S, Lee H, Tahk M-J (2003) Acceleration of the convergence speed of evolutionary algorithms using multi-layer neural networks. Eng Optim 35(1):91–102
103.
Zurück zum Zitat Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the first IEEE conference on evolutionary computation, IEEE world congress on computational intelligence, Piscataway, vol 1. IEEE Service Center, pp 82–87 Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the first IEEE conference on evolutionary computation, IEEE world congress on computational intelligence, Piscataway, vol 1. IEEE Service Center, pp 82–87
104.
Zurück zum Zitat Hsieh M-N, Chiang T-C, Fu L-C (2011) A hybrid constraint handling mechanism with differential evolution for constrained multiobjective optimization. In: 2011 IEEE congress on evolutionary computation (CEC’2011), New Orleans. IEEE Service Center, pp 1785–1792 Hsieh M-N, Chiang T-C, Fu L-C (2011) A hybrid constraint handling mechanism with differential evolution for constrained multiobjective optimization. In: 2011 IEEE congress on evolutionary computation (CEC’2011), New Orleans. IEEE Service Center, pp 1785–1792
105.
Zurück zum Zitat Huang B, Buckley B, Kechadi TM (2010) Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications. Expert Syst Appl 37(5):3638–3646 Huang B, Buckley B, Kechadi TM (2010) Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications. Expert Syst Appl 37(5):3638–3646
106.
Zurück zum Zitat Huband S, Hingston P, White L, Barone L (2003) An evolution strategy with probabilistic mutation for multi-objective optimisation. In: Proceedings of the 2003 congress on evolutionary computation (CEC’2003), Canberra, vol 3. IEEE Press, pp 2284–2291 Huband S, Hingston P, White L, Barone L (2003) An evolution strategy with probabilistic mutation for multi-objective optimisation. In: Proceedings of the 2003 congress on evolutionary computation (CEC’2003), Canberra, vol 3. IEEE Press, pp 2284–2291
107.
Zurück zum Zitat Husbands P (1994) Distributed coevolutionary genetic algorithms for multi-criteria and multi-constraint optimisation. In: Fogarty TC (ed) Evolutionary computing. AIS workshop. Selected papers. Lecture notes in computer science, vol 865. Springer, pp 150–165 Husbands P (1994) Distributed coevolutionary genetic algorithms for multi-criteria and multi-constraint optimisation. In: Fogarty TC (ed) Evolutionary computing. AIS workshop. Selected papers. Lecture notes in computer science, vol 865. Springer, pp 150–165
108.
Zurück zum Zitat Hüscken M, Jin Y, Sendhoff B (2005) Structure optimization of neural networks for aerodynamic optimization. Soft Comput 9(1):21–28 Hüscken M, Jin Y, Sendhoff B (2005) Structure optimization of neural networks for aerodynamic optimization. Soft Comput 9(1):21–28
109.
Zurück zum Zitat Ibaraki T, Nonobe K, Yagiura M (eds) (2005) Metaheuristics. Progress as real problem solvers. Springer, New York. ISBN:978-0-387-25382-4 Ibaraki T, Nonobe K, Yagiura M (eds) (2005) Metaheuristics. Progress as real problem solvers. Springer, New York. ISBN:978-0-387-25382-4
110.
Zurück zum Zitat Iordache R, Iordache S, Moldoveanu F (2014) A framework for the study of preference incorporation in multiobjective evolutionary algorithms. In: 2014 genetic and evolutionary computation conference (GECCO 2014), Vancouver. ACM Press, pp 621–628. ISBN:978-1-4503-2662-9 Iordache R, Iordache S, Moldoveanu F (2014) A framework for the study of preference incorporation in multiobjective evolutionary algorithms. In: 2014 genetic and evolutionary computation conference (GECCO 2014), Vancouver. ACM Press, pp 621–628. ISBN:978-1-4503-2662-9
111.
Zurück zum Zitat Iredi S, Merkle D, Middendorf M (2001) Bi-criterion optimization with multi colony ant algorithms. In: Zitzler E, Deb K, Thiele L, Coello Coello CA, Corne D (eds) First international conference on evolutionary multi-criterion optimization. Lecture notes in computer science, vol 1993. Springer, pp 359–372 Iredi S, Merkle D, Middendorf M (2001) Bi-criterion optimization with multi colony ant algorithms. In: Zitzler E, Deb K, Thiele L, Coello Coello CA, Corne D (eds) First international conference on evolutionary multi-criterion optimization. Lecture notes in computer science, vol 1993. Springer, pp 359–372
112.
Zurück zum Zitat Jain H, Deb K (2014) An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: handling constraints and extending to an adaptive approach. IEEE Trans Evol Comput 18(4):602–622 Jain H, Deb K (2014) An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: handling constraints and extending to an adaptive approach. IEEE Trans Evol Comput 18(4):602–622
113.
Zurück zum Zitat Jensen MT (2003) Reducing the run-time complexity of multiobjective EAs: the NSGA-II and other algorithms. IEEE Trans Evol Comput 7(5):503–515 Jensen MT (2003) Reducing the run-time complexity of multiobjective EAs: the NSGA-II and other algorithms. IEEE Trans Evol Comput 7(5):503–515
114.
Zurück zum Zitat Jin Y, Sendhoff B, Körner E (2005) Evolutionary multi-objective optimization for simultaneous generation of signal-type and symbol-type representations. In: Coello Coello CA, Hernández Aguirre A, Zitzler E (eds) Evolutionary multi-criterion optimization. Third international conference, EMO 2005, Guanajuato. Lecture notes in computer science, vol 3410. Springer, pp 752–766 Jin Y, Sendhoff B, Körner E (2005) Evolutionary multi-objective optimization for simultaneous generation of signal-type and symbol-type representations. In: Coello Coello CA, Hernández Aguirre A, Zitzler E (eds) Evolutionary multi-criterion optimization. Third international conference, EMO 2005, Guanajuato. Lecture notes in computer science, vol 3410. Springer, pp 752–766
115.
Zurück zum Zitat Kelaiaia R, Zaatri A, Company O (2012) Multiobjective optimization of 6-dof UPS parallel manipulators. Adv Robot 26(16):1885–1913 Kelaiaia R, Zaatri A, Company O (2012) Multiobjective optimization of 6-dof UPS parallel manipulators. Adv Robot 26(16):1885–1913
116.
Zurück zum Zitat Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann Publishers, San Francisco Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann Publishers, San Francisco
117.
Zurück zum Zitat Kita H, Yabumoto Y, Mori N, Nishikawa Y (1996) Multi-objective optimization by means of the thermodynamical genetic algorithm. In: Voigt H-M, Ebeling W, Rechenberg I, Schwefel H-P (eds) Parallel problem solving from nature—PPSN IV. Lecture notes in computer science, Berlin. Springer, pp 504–512 Kita H, Yabumoto Y, Mori N, Nishikawa Y (1996) Multi-objective optimization by means of the thermodynamical genetic algorithm. In: Voigt H-M, Ebeling W, Rechenberg I, Schwefel H-P (eds) Parallel problem solving from nature—PPSN IV. Lecture notes in computer science, Berlin. Springer, pp 504–512
118.
Zurück zum Zitat Knowles J (2006) ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans Evol Comput 10(1):50–66 Knowles J (2006) ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans Evol Comput 10(1):50–66
119.
Zurück zum Zitat Knowles JD, Corne DW (2000) Approximating the nondominated front using the pareto archived evolution strategy. Evol Comput 8(2):149–172 Knowles JD, Corne DW (2000) Approximating the nondominated front using the pareto archived evolution strategy. Evol Comput 8(2):149–172
120.
Zurück zum Zitat Knowles J, Corne D (2003) Properties of an adaptive archiving algorithm for storing nondominated vectors. IEEE Trans Evol Comput 7(2):100–116 Knowles J, Corne D (2003) Properties of an adaptive archiving algorithm for storing nondominated vectors. IEEE Trans Evol Comput 7(2):100–116
121.
Zurück zum Zitat Knowles J, Corne D (2007) Quantifying the effects of objective space dimension in evolutionary multiobjective optimization. In: Obayashi S, Deb K, Poloni C, Hiroyasu T, Murata T (eds) Evolutionary multi-criterion optimization, 4th international conference (EMO 2007), Matshushima. Lecture notes in computer science, vol 4403. Springer, pp 757–771 Knowles J, Corne D (2007) Quantifying the effects of objective space dimension in evolutionary multiobjective optimization. In: Obayashi S, Deb K, Poloni C, Hiroyasu T, Murata T (eds) Evolutionary multi-criterion optimization, 4th international conference (EMO 2007), Matshushima. Lecture notes in computer science, vol 4403. Springer, pp 757–771
122.
Zurück zum Zitat Lahsasna A, Ainon RN, Zainuddin R, Bulgiba A (2012) Design of a fuzzy-based decision support system for coronary heart disease diagnosis. J Med Syst 36(5):3293–3306 Lahsasna A, Ainon RN, Zainuddin R, Bulgiba A (2012) Design of a fuzzy-based decision support system for coronary heart disease diagnosis. J Med Syst 36(5):3293–3306
123.
Zurück zum Zitat Larzabal E, Cubillos JA, Larrea M, Irigoyen E, Valera JJ (2012) Soft computing testing in real industrial platforms for process intelligent control. In: Snášel V, Abraham A, Corchado ES (eds) Soft computing models in industrial and environmental applications, 7th international conference (SOCO’12). Advances in intelligent systems and computing, vol 188. Springer, Ostrava, pp 221–230 Larzabal E, Cubillos JA, Larrea M, Irigoyen E, Valera JJ (2012) Soft computing testing in real industrial platforms for process intelligent control. In: Snášel V, Abraham A, Corchado ES (eds) Soft computing models in industrial and environmental applications, 7th international conference (SOCO’12). Advances in intelligent systems and computing, vol 188. Springer, Ostrava, pp 221–230
124.
Zurück zum Zitat Laumanns M, Thiele L, Deb K, Zitzler E (2002) Combining convergence and diversity in evolutionary multi-objective optimization. Evol Comput 10(3):263–282. Fall Laumanns M, Thiele L, Deb K, Zitzler E (2002) Combining convergence and diversity in evolutionary multi-objective optimization. Evol Comput 10(3):263–282. Fall
125.
Zurück zum Zitat Laumanns M, Thiele L, Zitzler E (2004) Running time analysis of multiobjective evolutionary algorithms on Pseudo-Boolean functions. IEEE Trans Evol Comput 8(2):170–182 Laumanns M, Thiele L, Zitzler E (2004) Running time analysis of multiobjective evolutionary algorithms on Pseudo-Boolean functions. IEEE Trans Evol Comput 8(2):170–182
126.
Zurück zum Zitat Levene C, Correa E, Blanch EW, Goodacre R (2012) Enhancing surface enhanced raman scattering (SERS) detection of propranolol with multiobjective evolutionary optimization. Anal Chem 84(18):7899–7905 Levene C, Correa E, Blanch EW, Goodacre R (2012) Enhancing surface enhanced raman scattering (SERS) detection of propranolol with multiobjective evolutionary optimization. Anal Chem 84(18):7899–7905
127.
Zurück zum Zitat Li J-Q, Pan Q-K, Gao K-Z (2011) Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int J Adv Manuf Tech 55(9–12):1159–1169 Li J-Q, Pan Q-K, Gao K-Z (2011) Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int J Adv Manuf Tech 55(9–12):1159–1169
128.
Zurück zum Zitat López Jaimes A, Coello Coello CA, Chakraborty D (2008) Objective reduction using a feature selection technique. In: 2008 genetic and evolutionary computation conference (GECCO’2008), Atlanta. ACM Press, pp 674–680. ISBN:978-1-60558-131-6 López Jaimes A, Coello Coello CA, Chakraborty D (2008) Objective reduction using a feature selection technique. In: 2008 genetic and evolutionary computation conference (GECCO’2008), Atlanta. ACM Press, pp 674–680. ISBN:978-1-60558-131-6
129.
Zurück zum Zitat López Jaimes A, Santana Quintero LV, Coello Coello CA (2009) Ranking methods in many-objective evolutionary algorithms. In: Chiong R (ed) Nature-inspired algorithms for optimisation. Springer, Berlin, pp 413–434. ISBN:978-3-642-00266-3 López Jaimes A, Santana Quintero LV, Coello Coello CA (2009) Ranking methods in many-objective evolutionary algorithms. In: Chiong R (ed) Nature-inspired algorithms for optimisation. Springer, Berlin, pp 413–434. ISBN:978-3-642-00266-3
130.
Zurück zum Zitat Luh G-C, Chueh C-H, Liu W-W (2003) MOIA: multi-objective immune algorithm. Eng Optim 35(2):143–164 Luh G-C, Chueh C-H, Liu W-W (2003) MOIA: multi-objective immune algorithm. Eng Optim 35(2):143–164
131.
Zurück zum Zitat Mahmoodabadi MJ, Arabani Mostaghim S, Bagheri A, Nariman-zadeh N (2013) Pareto optimal design of the decoupled sliding mode controller for an inverted pendulum system and its stability simulation via Java programming. Math Comput Model 57(5–6):1070–1082 Mahmoodabadi MJ, Arabani Mostaghim S, Bagheri A, Nariman-zadeh N (2013) Pareto optimal design of the decoupled sliding mode controller for an inverted pendulum system and its stability simulation via Java programming. Math Comput Model 57(5–6):1070–1082
132.
Zurück zum Zitat Menchaca-Mendez A, Coello Coello CA (2013) Selection operators based on maximin fitness function for multi-objective evolutionary algorithms. In: Purshouse RC, Fleming PJ, Fonseca CM, Greco S, Shaw J (eds) Evolutionary multi-criterion optimization, 7th international conference (EMO 2013). Lecture notes in computer science, vol 7811, Sheffield. Springer, pp 215–229 Menchaca-Mendez A, Coello Coello CA (2013) Selection operators based on maximin fitness function for multi-objective evolutionary algorithms. In: Purshouse RC, Fleming PJ, Fonseca CM, Greco S, Shaw J (eds) Evolutionary multi-criterion optimization, 7th international conference (EMO 2013). Lecture notes in computer science, vol 7811, Sheffield. Springer, pp 215–229
133.
Zurück zum Zitat Mezura-Montes E, Coello Coello CA (2008) Constrained optimization via multiobjective evolutionary algorithms. In: Knowles J, Corne D, Deb K (eds) Multi-objective problem solving from nature: from concepts to applications. Springer, Berlin, pp 53–75. ISBN:978-3-540-72963-1 Mezura-Montes E, Coello Coello CA (2008) Constrained optimization via multiobjective evolutionary algorithms. In: Knowles J, Corne D, Deb K (eds) Multi-objective problem solving from nature: from concepts to applications. Springer, Berlin, pp 53–75. ISBN:978-3-540-72963-1
134.
Zurück zum Zitat Mezura-Montes E, Reyes-Sierra M, Coello Coello CA (2008) Multi-objective optimization using differential evolution: a survey of the state-of-the-art. In: Chakraborty UK (ed) Advances in differential evolution. Springer, Berlin, pp 173–196. ISBN:978-3-540- 68827-3 Mezura-Montes E, Reyes-Sierra M, Coello Coello CA (2008) Multi-objective optimization using differential evolution: a survey of the state-of-the-art. In: Chakraborty UK (ed) Advances in differential evolution. Springer, Berlin, pp 173–196. ISBN:978-3-540- 68827-3
135.
Zurück zum Zitat Miettinen KM (1999) Nonlinear multiobjective optimization. Kluwer Academic Publishers, Boston Miettinen KM (1999) Nonlinear multiobjective optimization. Kluwer Academic Publishers, Boston
136.
Zurück zum Zitat Mishra BSP, Dehuri S, Mall R, Ghosh A (2011) Parallel single and multiple objectives genetic algorithms: a survey. Int J Appl Evol Comput 2(2):21–57 Mishra BSP, Dehuri S, Mall R, Ghosh A (2011) Parallel single and multiple objectives genetic algorithms: a survey. Int J Appl Evol Comput 2(2):21–57
137.
Zurück zum Zitat Moncayo-Martinez LA, Zhang DZ (2011) Multi-objective ant colony optimisation: a meta-heuristic approach to supply chain design. Int J Prod Econ 131(1):407–420 Moncayo-Martinez LA, Zhang DZ (2011) Multi-objective ant colony optimisation: a meta-heuristic approach to supply chain design. Int J Prod Econ 131(1):407–420
138.
Zurück zum Zitat Montaño AA, Coello Coello CA, Mezura-Montes E (2010) MODE-LD+SS: a novel differential evolution algorithm incorporating local dominance and scalar selection mechanisms for multi-objective optimization. In: 2010 IEEE congress on evolutionary computation (CEC’2010), Barcelona. IEEE Press, pp 3284–3291 Montaño AA, Coello Coello CA, Mezura-Montes E (2010) MODE-LD+SS: a novel differential evolution algorithm incorporating local dominance and scalar selection mechanisms for multi-objective optimization. In: 2010 IEEE congress on evolutionary computation (CEC’2010), Barcelona. IEEE Press, pp 3284–3291
139.
Zurück zum Zitat Moore J, Chapman R, Dozier G (2000) Multiobjective particle swarm optimization. In: Turner AJ (ed) Proceedings of the 38th annual southeast regional conference, Clemson. ACM Press, pp 56–57 Moore J, Chapman R, Dozier G (2000) Multiobjective particle swarm optimization. In: Turner AJ (ed) Proceedings of the 38th annual southeast regional conference, Clemson. ACM Press, pp 56–57
140.
Zurück zum Zitat Mora AM, Garcia-Sanchez P, Merelo JJ, Castillo PA (2013) Pareto-based multi-colony multi-objective ant colony optimization algorithms: an island model proposal. Soft Comput 17(7):1175–1207 Mora AM, Garcia-Sanchez P, Merelo JJ, Castillo PA (2013) Pareto-based multi-colony multi-objective ant colony optimization algorithms: an island model proposal. Soft Comput 17(7):1175–1207
141.
Zurück zum Zitat Narayanan L, Subramanian B, Arokiaswami A, Iruthayarajan MW (2012) Optimal placement of mobile antenna in an urban area using evolutionary multiobjective optimization. Microw Opt Technol Lett 54(3):737–743 Narayanan L, Subramanian B, Arokiaswami A, Iruthayarajan MW (2012) Optimal placement of mobile antenna in an urban area using evolutionary multiobjective optimization. Microw Opt Technol Lett 54(3):737–743
142.
Zurück zum Zitat Nebro AJ, Durillo JJ, Garcia-Nieto J, Coello Coello CA, Luna F, Alba E (2009) SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: 2009 IEEE symposium on computational intelligence in multi-criteria decision-making (MCDM’2009), Nashville. IEEE Press, pp 66–73. ISBN:978-1-4244-2764-2 Nebro AJ, Durillo JJ, Garcia-Nieto J, Coello Coello CA, Luna F, Alba E (2009) SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: 2009 IEEE symposium on computational intelligence in multi-criteria decision-making (MCDM’2009), Nashville. IEEE Press, pp 66–73. ISBN:978-1-4244-2764-2
143.
Zurück zum Zitat Neumann F (2007) Expected runtimes of a simple evolutionary algorithm for the multi-objective minimum spanning tree problem. Eur J Oper Res 181(3):1620–1629 Neumann F (2007) Expected runtimes of a simple evolutionary algorithm for the multi-objective minimum spanning tree problem. Eur J Oper Res 181(3):1620–1629
144.
Zurück zum Zitat Neumann F (2012) Computational complexity analysis of multi-objective genetic programming. In: 2012 genetic and evolutionary computation conference (GECCO’2012), Philadelphia. ACM Press, pp 799–806. ISBN:978-1-4503-1177-9 Neumann F (2012) Computational complexity analysis of multi-objective genetic programming. In: 2012 genetic and evolutionary computation conference (GECCO’2012), Philadelphia. ACM Press, pp 799–806. ISBN:978-1-4503-1177-9
145.
Zurück zum Zitat Ning X, Lam KC (2013) Cost-safety trade-off in unequal-area construction site layout planning. Autom Constr 32:96–103 Ning X, Lam KC (2013) Cost-safety trade-off in unequal-area construction site layout planning. Autom Constr 32:96–103
146.
Zurück zum Zitat Olmo JL, Romero JR, Ventura S (2012) Classification rule mining using ant programming guided by grammar with multiple Pareto fronts. Soft Comput 16(12):2143–2163 Olmo JL, Romero JR, Ventura S (2012) Classification rule mining using ant programming guided by grammar with multiple Pareto fronts. Soft Comput 16(12):2143–2163
147.
Zurück zum Zitat Ong YS, Nair PB, Keane AJ, Wong KW (2004) Surrogate-assisted evolutionary optimization frameworks for high-fidelity engineering design problems. In: Jin Y (ed) Knowledge incorporation in evolutionary computation. Studies in fuzziness and soft computing. Springer, Berlin, Germany, pp 307–332 Ong YS, Nair PB, Keane AJ, Wong KW (2004) Surrogate-assisted evolutionary optimization frameworks for high-fidelity engineering design problems. In: Jin Y (ed) Knowledge incorporation in evolutionary computation. Studies in fuzziness and soft computing. Springer, Berlin, Germany, pp 307–332
148.
Zurück zum Zitat Oyama A, Shimoyama K, Fujii K (2007) New constraint-handling method for multi-objective and multi-constraint evolutionary optimization. Trans Jpn Soc Aeronaut Space Sci 50(167):56–62 Oyama A, Shimoyama K, Fujii K (2007) New constraint-handling method for multi-objective and multi-constraint evolutionary optimization. Trans Jpn Soc Aeronaut Space Sci 50(167):56–62
149.
Zurück zum Zitat Pacheco J, Marti R (2006) Tabu search for a multi-objective routing problem. J Oper Res Soc 57(1):29–37 Pacheco J, Marti R (2006) Tabu search for a multi-objective routing problem. J Oper Res Soc 57(1):29–37
150.
Zurück zum Zitat Pardalos PM, Siskos Y, Zopounidis C (eds) (1995) Advances in multiciteria analysis. Springer-Science+Business Media, B.V. ISBN:978-1-4419-4748-2 Pardalos PM, Siskos Y, Zopounidis C (eds) (1995) Advances in multiciteria analysis. Springer-Science+Business Media, B.V. ISBN:978-1-4419-4748-2
151.
Zurück zum Zitat Pardalos PM, Žilinskas A, Žilinskas J (2017) Non-convex multi-objective optimization. Springer, Cham. ISBN:978-3-319-61005-4 Pardalos PM, Žilinskas A, Žilinskas J (2017) Non-convex multi-objective optimization. Springer, Cham. ISBN:978-3-319-61005-4
152.
Zurück zum Zitat Pareto V (1896) Cours D’Economie Politique, vol I and II. F. Rouge, Lausanne Pareto V (1896) Cours D’Economie Politique, vol I and II. F. Rouge, Lausanne
153.
Zurück zum Zitat Parsopoulos KE, Taoulis DK, Pavlidis NG, Plagianakos VP, Vrahatis MN (2004) Vector evaluated differential evolution for multiobjective optimization. In: 2004 congress on evolutionary computation (CEC’2004), Portland, vol 1. IEEE Service Center, pp 204–211 Parsopoulos KE, Taoulis DK, Pavlidis NG, Plagianakos VP, Vrahatis MN (2004) Vector evaluated differential evolution for multiobjective optimization. In: 2004 congress on evolutionary computation (CEC’2004), Portland, vol 1. IEEE Service Center, pp 204–211
154.
Zurück zum Zitat Phan DH, Suzuki J (2013) R2-IBEA: R2 indicator based evolutionary algorithm for multiobjective optimization. In: 2013 IEEE congress on evolutionary computation (CEC’2013), Cancún. IEEE Press, pp 1836–1845. ISBN:978-1-4799-0454-9 Phan DH, Suzuki J (2013) R2-IBEA: R2 indicator based evolutionary algorithm for multiobjective optimization. In: 2013 IEEE congress on evolutionary computation (CEC’2013), Cancún. IEEE Press, pp 1836–1845. ISBN:978-1-4799-0454-9
155.
Zurück zum Zitat Pierrard T, Coello Coello CA (2012) A multi-objective artificial immune system based on hypervolume. In: Coelo Coello CA, Greensmith J, Krasnogor N, Liò P, Nicosia G, Pavone M (eds) Artificial immune systems, 11th international conference (ICARIS 2012). Lecture notes in computer science, vol 7597. Springer, Taormina, pp 14–27. ISBN:978-3-642- 33756-7 Pierrard T, Coello Coello CA (2012) A multi-objective artificial immune system based on hypervolume. In: Coelo Coello CA, Greensmith J, Krasnogor N, Liò P, Nicosia G, Pavone M (eds) Artificial immune systems, 11th international conference (ICARIS 2012). Lecture notes in computer science, vol 7597. Springer, Taormina, pp 14–27. ISBN:978-3-642- 33756-7
156.
Zurück zum Zitat Pierret S (1999) Turbomachinery blade design using a Navier-Stokes solver and artificial neural network. ASME J Turbomach 121(3):326–332 Pierret S (1999) Turbomachinery blade design using a Navier-Stokes solver and artificial neural network. ASME J Turbomach 121(3):326–332
157.
Zurück zum Zitat Pilato C, Loiacono D, Tumeo A, Ferrandi F, Lanzi PL, Sciuto D (2010) Speeding-up expensive evaluations in high-level synthesis using solution modeling and fitness inheritance. In: Tenne Y, Goh C-K (eds) Computational intelligence in expensive optimization problems. Springer, Berlin, pp 701–723. ISBN:978-3-642-10700-9 Pilato C, Loiacono D, Tumeo A, Ferrandi F, Lanzi PL, Sciuto D (2010) Speeding-up expensive evaluations in high-level synthesis using solution modeling and fitness inheritance. In: Tenne Y, Goh C-K (eds) Computational intelligence in expensive optimization problems. Springer, Berlin, pp 701–723. ISBN:978-3-642-10700-9
158.
Zurück zum Zitat Rahimi-Vahed AR, Javadi B, Rabbani M, Tavakkoli-Moghaddam R (2008) A multi-objective scatter search for a bi-criteria no-wait flow shop scheduling problem. Eng Optim 40(4): 331–346 Rahimi-Vahed AR, Javadi B, Rabbani M, Tavakkoli-Moghaddam R (2008) A multi-objective scatter search for a bi-criteria no-wait flow shop scheduling problem. Eng Optim 40(4): 331–346
159.
Zurück zum Zitat Rakshit P, Konar A, Nagar AK (2014) Artificial bee colony induced multi-objective optimization in presence of noise. In: 2014 IEEE congress on evolutionary computation (CEC’2014), Beijing. IEEE Press, pp 3176–3183. ISBN:978-1-4799-1488-3 Rakshit P, Konar A, Nagar AK (2014) Artificial bee colony induced multi-objective optimization in presence of noise. In: 2014 IEEE congress on evolutionary computation (CEC’2014), Beijing. IEEE Press, pp 3176–3183. ISBN:978-1-4799-1488-3
160.
Zurück zum Zitat Rao ARM, Lakshmi K (2008) Multi-objective scatter search algorithm for combinatorial optimisation. In: Thulasiram R (ed) ADCOM: 2008 16th international conference on advanced computing and communications, Chennai. IEEE Press, pp 303–308. ISBN:978-1-4244- 2962-2 Rao ARM, Lakshmi K (2008) Multi-objective scatter search algorithm for combinatorial optimisation. In: Thulasiram R (ed) ADCOM: 2008 16th international conference on advanced computing and communications, Chennai. IEEE Press, pp 303–308. ISBN:978-1-4244- 2962-2
161.
Zurück zum Zitat Rao BS, Vaisakh K (2013) Multi-objective adaptive clonal selection algorithm for solving environmental/economic dispatch and OPF problems with load uncertainty. Int J Electr Power Energy Syst 53:390–408 Rao BS, Vaisakh K (2013) Multi-objective adaptive clonal selection algorithm for solving environmental/economic dispatch and OPF problems with load uncertainty. Int J Electr Power Energy Syst 53:390–408
162.
Zurück zum Zitat Rasheed K, Ni X, Vattam S (2005) Comparison of methods for developing dynamic reduced models for design optimization. Soft Comput 9(1):29–37 Rasheed K, Ni X, Vattam S (2005) Comparison of methods for developing dynamic reduced models for design optimization. Soft Comput 9(1):29–37
163.
Zurück zum Zitat Ratle A (1998) Accelerating the convergence of evolutionary algorithms by fitness landscape approximation. In: Eiben AE, Bäck T, Schoenauer M, Schwefel H-P (eds) Parallel problem solving from nature—PPSN V, 5th international conference, Amsterdam. Lecture notes in computer science, vol 1498. Springer, pp 87–96 Ratle A (1998) Accelerating the convergence of evolutionary algorithms by fitness landscape approximation. In: Eiben AE, Bäck T, Schoenauer M, Schwefel H-P (eds) Parallel problem solving from nature—PPSN V, 5th international conference, Amsterdam. Lecture notes in computer science, vol 1498. Springer, pp 87–96
164.
Zurück zum Zitat Reyes Sierra M, Coello Coello CA (2005) Fitness inheritance in multi-objective particle swarm optimization. In: 2005 IEEE swarm intelligence symposium (SIS’05), Pasadena. IEEE Press, pp 116–123 Reyes Sierra M, Coello Coello CA (2005) Fitness inheritance in multi-objective particle swarm optimization. In: 2005 IEEE swarm intelligence symposium (SIS’05), Pasadena. IEEE Press, pp 116–123
165.
Zurück zum Zitat Reyes Sierra M, Coello Coello CA (2005) Improving PSO-based multi-objective optimization using crowding, mutation and 𝜖-dominance. In: Coello Coello CA, Hernández Aguirre A, Zitzler E (eds) Evolutionary multi-criterion optimization. Third international conference (EMO 2005), Guanajuato. Lecture notes in computer science, vol 3410. Springer, pp 505–519 Reyes Sierra M, Coello Coello CA (2005) Improving PSO-based multi-objective optimization using crowding, mutation and 𝜖-dominance. In: Coello Coello CA, Hernández Aguirre A, Zitzler E (eds) Evolutionary multi-criterion optimization. Third international conference (EMO 2005), Guanajuato. Lecture notes in computer science, vol 3410. Springer, pp 505–519
166.
Zurück zum Zitat Reyes Sierra M, Coello Coello CA (2005) A study of fitness inheritance and approximation techniques for multi-objective particle swarm optimization. In: 2005 IEEE congress on evolutionary computation (CEC’2005), Edinburgh, vol 1. IEEE Service Center, pp 65–72 Reyes Sierra M, Coello Coello CA (2005) A study of fitness inheritance and approximation techniques for multi-objective particle swarm optimization. In: 2005 IEEE congress on evolutionary computation (CEC’2005), Edinburgh, vol 1. IEEE Service Center, pp 65–72
167.
Zurück zum Zitat Reyes-Sierra M, Coello Coello CA (2006) Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intell Res 2(3):287–308 Reyes-Sierra M, Coello Coello CA (2006) Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intell Res 2(3):287–308
168.
Zurück zum Zitat Rodríguez Villalobos CA, Coello Coello CA (2012) A new multi-objective evolutionary algorithm based on a performance assessment indicator. In: 2012 genetic and evolutionary computation conference (GECCO’2012), Philadelphia. ACM Press, pp 505–512. ISBN:978-1-4503-1177-9 Rodríguez Villalobos CA, Coello Coello CA (2012) A new multi-objective evolutionary algorithm based on a performance assessment indicator. In: 2012 genetic and evolutionary computation conference (GECCO’2012), Philadelphia. ACM Press, pp 505–512. ISBN:978-1-4503-1177-9
169.
Zurück zum Zitat Rohling G (2008) Methods for decreasing the number of objective evaluations for independent computationally expensive objective problems. In: 2008 congress on evolutionary computation (CEC’2008), Hong Kong. IEEE Service Center, pp 3304–3309 Rohling G (2008) Methods for decreasing the number of objective evaluations for independent computationally expensive objective problems. In: 2008 congress on evolutionary computation (CEC’2008), Hong Kong. IEEE Service Center, pp 3304–3309
170.
Zurück zum Zitat Romero CEM, Manzanares EM (1999) MOAQ an ant-Q algorithm for multiple objective optimization problems. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Genetic and evolutionary computing conference (GECCO’99), San Francisco, vol 1. Morgan Kaufmann, pp 894–901 Romero CEM, Manzanares EM (1999) MOAQ an ant-Q algorithm for multiple objective optimization problems. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Genetic and evolutionary computing conference (GECCO’99), San Francisco, vol 1. Morgan Kaufmann, pp 894–901
171.
Zurück zum Zitat Romero-Garcia V, Sanchez-Perez JV, Garcia-Raffi LM (2012) Molding the acoustic attenuation in quasi-ordered structures: experimental realization. Appl Phys Express 5(8). Article number:087301 Romero-Garcia V, Sanchez-Perez JV, Garcia-Raffi LM (2012) Molding the acoustic attenuation in quasi-ordered structures: experimental realization. Appl Phys Express 5(8). Article number:087301
172.
Zurück zum Zitat Ronco CCD, Ponza R, Benini E (2014) Aerodynamic shape optimization in aeronautics: a fast and effective multi-objective approach. Arch Comput Methods Eng 21(3):189–271 Ronco CCD, Ponza R, Benini E (2014) Aerodynamic shape optimization in aeronautics: a fast and effective multi-objective approach. Arch Comput Methods Eng 21(3):189–271
173.
Zurück zum Zitat Rosenberg R (1967) Simulation of genetic populations with biochemical properties. PhD thesis, Department of Communication Sciences, University of Michigan, Ann Arbor Rosenberg R (1967) Simulation of genetic populations with biochemical properties. PhD thesis, Department of Communication Sciences, University of Michigan, Ann Arbor
174.
Zurück zum Zitat Rudolph G, Agapie A (2000) Convergence properties of some multi-objective evolutionary algorithms. In: Proceedings of the 2000 conference on evolutionary computation, Piscataway, vol 2. IEEE Press, pp 1010–1016 Rudolph G, Agapie A (2000) Convergence properties of some multi-objective evolutionary algorithms. In: Proceedings of the 2000 conference on evolutionary computation, Piscataway, vol 2. IEEE Press, pp 1010–1016
175.
Zurück zum Zitat Saha I, Maulik U, Bandyopadhyay S, Plewczynski D (2011) Unsupervised and supervised learning approaches together for microarray analysis. Fundamenta Informaticae 106(1): 45–73 Saha I, Maulik U, Bandyopadhyay S, Plewczynski D (2011) Unsupervised and supervised learning approaches together for microarray analysis. Fundamenta Informaticae 106(1): 45–73
176.
Zurück zum Zitat Sahoo NC, Ganguly S, Das D (2012) Fuzzy-Pareto-dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization. Expert Syst Appl 39(1):881–893 Sahoo NC, Ganguly S, Das D (2012) Fuzzy-Pareto-dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization. Expert Syst Appl 39(1):881–893
177.
Zurück zum Zitat Santana-Quintero LV, Arias Montaño A, Coello Coello CA (2010) A review of techniques for handling expensive functions in evolutionary multi-objective optimization. In: Tenne Y, Goh C-K (eds) Computational intelligence in expensive optimization problems. Springer, Berlin, pp 29–59. ISBN:978-3-642-10700-9 Santana-Quintero LV, Arias Montaño A, Coello Coello CA (2010) A review of techniques for handling expensive functions in evolutionary multi-objective optimization. In: Tenne Y, Goh C-K (eds) Computational intelligence in expensive optimization problems. Springer, Berlin, pp 29–59. ISBN:978-3-642-10700-9
178.
Zurück zum Zitat Schaffer JD (1984) Multiple objective optimization with vector evaluated genetic algorithms. PhD thesis, Vanderbilt University, Nashville Schaffer JD (1984) Multiple objective optimization with vector evaluated genetic algorithms. PhD thesis, Vanderbilt University, Nashville
179.
Zurück zum Zitat Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Genetic algorithms and their applications: proceedings of the first international conference on genetic algorithms. Lawrence Erlbaum, pp 93–100 Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Genetic algorithms and their applications: proceedings of the first international conference on genetic algorithms. Lawrence Erlbaum, pp 93–100
180.
Zurück zum Zitat Schuetze O, Laumanns M, Tantar E, Coello Coello CA, Talbi E (2007) Convergence of stochastic search algorithms to gap-free Pareto front approximations. In: Thierens D (ed) 2007 genetic and evolutionary computation conference (GECCO’2007), London, vol 1. ACM Press, pp 892–899 Schuetze O, Laumanns M, Tantar E, Coello Coello CA, Talbi E (2007) Convergence of stochastic search algorithms to gap-free Pareto front approximations. In: Thierens D (ed) 2007 genetic and evolutionary computation conference (GECCO’2007), London, vol 1. ACM Press, pp 892–899
181.
Zurück zum Zitat Schuetze O, Laumanns M, Tantar E, Coello Coello CA, Talbi E (2010) Computing gap free Pareto front approximations with stochastic search algorithms. Evol Comput 18(1):65–96. Spring Schuetze O, Laumanns M, Tantar E, Coello Coello CA, Talbi E (2010) Computing gap free Pareto front approximations with stochastic search algorithms. Evol Comput 18(1):65–96. Spring
182.
Zurück zum Zitat Schütze O, Lara A, Coello Coello CA (2011) On the influence of the number of objectives on the hardness of a multiobjective optimization problem. IEEE Trans Evol Comput 15(4):444–455 Schütze O, Lara A, Coello Coello CA (2011) On the influence of the number of objectives on the hardness of a multiobjective optimization problem. IEEE Trans Evol Comput 15(4):444–455
183.
Zurück zum Zitat Schütze O, Esquivel X, Lara A, Coello Coello CA (2012) Using the averaged hausdorff distance as a performance measure in evolutionary multiobjective optimization. IEEE Trans Evol Comput 16(4):504–522 Schütze O, Esquivel X, Lara A, Coello Coello CA (2012) Using the averaged hausdorff distance as a performance measure in evolutionary multiobjective optimization. IEEE Trans Evol Comput 16(4):504–522
184.
Zurück zum Zitat Schwefel H-P (1965) Kybernetische evolution als strategie der experimentellen forschung in der strömungstechnik. Dipl.-Ing. thesis (in German) Schwefel H-P (1965) Kybernetische evolution als strategie der experimentellen forschung in der strömungstechnik. Dipl.-Ing. thesis (in German)
185.
Zurück zum Zitat Schwefel H-P (1981) Numerical optimization of computer models. Wiley, Chichester Schwefel H-P (1981) Numerical optimization of computer models. Wiley, Chichester
186.
Zurück zum Zitat Sharifi S, Massoudieh A (2012) A novel hybrid mechanistic-data-driven model identification framework using NSGA-II. J Hydroinf 14(3):697–715 Sharifi S, Massoudieh A (2012) A novel hybrid mechanistic-data-driven model identification framework using NSGA-II. J Hydroinf 14(3):697–715
187.
Zurück zum Zitat Sharma D, Collet P (2013) Implementation techniques for massively parallel multi-objective optimization. In: Tsutsui S, Collet P (eds) Massively parallel evolutionary computation on GPGPUs. Springer, pp 267–286. ISBN:978-3-642-37958-1 Sharma D, Collet P (2013) Implementation techniques for massively parallel multi-objective optimization. In: Tsutsui S, Collet P (eds) Massively parallel evolutionary computation on GPGPUs. Springer, pp 267–286. ISBN:978-3-642-37958-1
188.
Zurück zum Zitat Shaw KJ, Fleming PJ (1996) Initial study of practical multi-objective genetic algorithms for scheduling the production of chilled ready meals. In: Proceedings of mendel’96, the 2nd international mendel conference on genetic algorithms, Brno Shaw KJ, Fleming PJ (1996) Initial study of practical multi-objective genetic algorithms for scheduling the production of chilled ready meals. In: Proceedings of mendel’96, the 2nd international mendel conference on genetic algorithms, Brno
189.
Zurück zum Zitat Singh HK, Isaacs A, Ray T, Smith W (2008) A simulated annealing algorithm for constrained multi-objective optimization. In: 2008 congress on evolutionary computation (CEC’2008), Hong Kong. IEEE Service Center, pp 1655–1662 Singh HK, Isaacs A, Ray T, Smith W (2008) A simulated annealing algorithm for constrained multi-objective optimization. In: 2008 congress on evolutionary computation (CEC’2008), Hong Kong. IEEE Service Center, pp 1655–1662
190.
Zurück zum Zitat Smith KI (2006) A study of simulated annealing techniques for multi-objective optimisation. PhD thesis, University of Exeter Smith KI (2006) A study of simulated annealing techniques for multi-objective optimisation. PhD thesis, University of Exeter
191.
Zurück zum Zitat Smith RE, Forrest S, Perelson AS (1992) Searching for diverse, cooperative populations with genetic algorithms. Technical report TCGA No. 92002, University of Alabama, Tuscaloosa Smith RE, Forrest S, Perelson AS (1992) Searching for diverse, cooperative populations with genetic algorithms. Technical report TCGA No. 92002, University of Alabama, Tuscaloosa
192.
Zurück zum Zitat Smith RE, Forrest S, Perelson AS (1993) Population diversity in an immune system model: implications for genetic search. In: Whitley LD (ed) Foundations of genetic algorithms 2. Morgan Kaufmann Publishers, San Mateo, pp 153–165 Smith RE, Forrest S, Perelson AS (1993) Population diversity in an immune system model: implications for genetic search. In: Whitley LD (ed) Foundations of genetic algorithms 2. Morgan Kaufmann Publishers, San Mateo, pp 153–165
193.
Zurück zum Zitat Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248. Fall Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248. Fall
194.
Zurück zum Zitat Suman B, Kumar P (2006) A survey of simulated annealing as a tool for single and multiobjective optimization. J Oper Res Soc 57(10):1143–1160CrossRef Suman B, Kumar P (2006) A survey of simulated annealing as a tool for single and multiobjective optimization. J Oper Res Soc 57(10):1143–1160CrossRef
195.
Zurück zum Zitat Surry PD, Radcliffe NJ (1997) The COMOGA method: constrained optimisation by multiobjective genetic algorithms. Control Cybern 26(3):391–412MATH Surry PD, Radcliffe NJ (1997) The COMOGA method: constrained optimisation by multiobjective genetic algorithms. Control Cybern 26(3):391–412MATH
196.
Zurück zum Zitat Sweetapple C, Fu G, Butler D (2014) Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions. Water Res 55:52–62CrossRef Sweetapple C, Fu G, Butler D (2014) Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions. Water Res 55:52–62CrossRef
197.
Zurück zum Zitat Tagawa K, Shimizu H, Nakamura H (2011) Indicator-based differential evolution using exclusive hypervolume approximation and parallelization for multi-core processors. In: 2011 genetic and evolutionary computation conference (GECCO’2011), Dublin. ACM Press, pp 657–664 Tagawa K, Shimizu H, Nakamura H (2011) Indicator-based differential evolution using exclusive hypervolume approximation and parallelization for multi-core processors. In: 2011 genetic and evolutionary computation conference (GECCO’2011), Dublin. ACM Press, pp 657–664
198.
Zurück zum Zitat Talbi E-G (ed) (2009) Metaheuristics. From design to implementation. Wiley, New Jersey. ISBN:978-0-470-27858-1MATH Talbi E-G (ed) (2009) Metaheuristics. From design to implementation. Wiley, New Jersey. ISBN:978-0-470-27858-1MATH
199.
Zurück zum Zitat Talukder AKMKA, Kirley M, Buyya R (2009) Multiobjective differential evolution for scheduling workflow applications on global Grids. Concurrency Comput-Pract Exp 21(13):1742–1756CrossRef Talukder AKMKA, Kirley M, Buyya R (2009) Multiobjective differential evolution for scheduling workflow applications on global Grids. Concurrency Comput-Pract Exp 21(13):1742–1756CrossRef
200.
Zurück zum Zitat Tan KC, Lee TH, Khor EF (2001) Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization. IEEE Trans Evol Comput 5(6):565–588CrossRef Tan KC, Lee TH, Khor EF (2001) Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization. IEEE Trans Evol Comput 5(6):565–588CrossRef
201.
Zurück zum Zitat Tan KC, Khor EF, Lee TH (2005) Multiobjective evolutionary algorithms and applications. Springer, London. ISBN:1-85233-836-9MATH Tan KC, Khor EF, Lee TH (2005) Multiobjective evolutionary algorithms and applications. Springer, London. ISBN:1-85233-836-9MATH
202.
Zurück zum Zitat Toscano Pulido G, Coello Coello CA (2003) The micro genetic algorithm 2: towards online adaptation in evolutionary multiobjective optimization. In: Fonseca CM, Fleming PJ, Zitzler E, Deb K, Thiele L (eds) Evolutionary multi-criterion optimization. Second international conference (EMO 2003), Faro. Lecture notes in computer science, vol 2632. Springer, pp 252–266 Toscano Pulido G, Coello Coello CA (2003) The micro genetic algorithm 2: towards online adaptation in evolutionary multiobjective optimization. In: Fonseca CM, Fleming PJ, Zitzler E, Deb K, Thiele L (eds) Evolutionary multi-criterion optimization. Second international conference (EMO 2003), Faro. Lecture notes in computer science, vol 2632. Springer, pp 252–266
203.
Zurück zum Zitat Toscano Pulido G, Coello Coello CA (2004) using clustering techniques to improve the performance of a particle swarm optimizer. In: Deb K et al (ed) Genetic and evolutionary computation–GECCO 2004. Proceedings of the genetic and evolutionary computation conference. Part I, Seattle, Washington. Lecture notes in computer science, vol 3102. Springer, pp 225–237 Toscano Pulido G, Coello Coello CA (2004) using clustering techniques to improve the performance of a particle swarm optimizer. In: Deb K et al (ed) Genetic and evolutionary computation–GECCO 2004. Proceedings of the genetic and evolutionary computation conference. Part I, Seattle, Washington. Lecture notes in computer science, vol 3102. Springer, pp 225–237
204.
Zurück zum Zitat Tušar T, Filipič B (2007) Differential evolution versus genetic algorithms in multiobjective optimization. In: Obayashi S, Deb K, Poloni C, Hiroyasu T, Murata T (eds) Evolutionary multi-criterion optimization, 4th international conference (EMO 2007), Matshushima. Lecture notes in computer science, vol 4403. Springer, pp 257–271 Tušar T, Filipič B (2007) Differential evolution versus genetic algorithms in multiobjective optimization. In: Obayashi S, Deb K, Poloni C, Hiroyasu T, Murata T (eds) Evolutionary multi-criterion optimization, 4th international conference (EMO 2007), Matshushima. Lecture notes in computer science, vol 4403. Springer, pp 257–271
205.
Zurück zum Zitat Ulmer H, Streicher F, Zell A (2003) Model-assisted steady-state evolution strategies. In: Cantú-Paz E et al (ed) Genetic and evolutionary computation—GECCO 2003. Proceedings, Part I. Lecture notes in computer science, vol 2723. Springer, pp 610–621 Ulmer H, Streicher F, Zell A (2003) Model-assisted steady-state evolution strategies. In: Cantú-Paz E et al (ed) Genetic and evolutionary computation—GECCO 2003. Proceedings, Part I. Lecture notes in computer science, vol 2723. Springer, pp 610–621
206.
Zurück zum Zitat Ulmer H, Streichert F, Zell A (2003) Evolution startegies assisted by Gaussian processes with improved pre-selection criterion. In: Proceedings of the 2003 IEEE congress on evolutionary computation (CEC’2003), Canberra, vol 1. IEEE Press, pp 692–699 Ulmer H, Streichert F, Zell A (2003) Evolution startegies assisted by Gaussian processes with improved pre-selection criterion. In: Proceedings of the 2003 IEEE congress on evolutionary computation (CEC’2003), Canberra, vol 1. IEEE Press, pp 692–699
207.
Zurück zum Zitat Vargas DEC, Lemonge ACC, Barbosa HJC, Bernardino HS (2013) Differential evolution with the adaptive penalty method for constrained multiobjective optimization. In: 2013 IEEE congress on evolutionary computation (CEC’2013), Cancún. IEEE Press, pp 1342–1349. ISBN:978-1-4799-0454-9CrossRef Vargas DEC, Lemonge ACC, Barbosa HJC, Bernardino HS (2013) Differential evolution with the adaptive penalty method for constrained multiobjective optimization. In: 2013 IEEE congress on evolutionary computation (CEC’2013), Cancún. IEEE Press, pp 1342–1349. ISBN:978-1-4799-0454-9CrossRef
208.
Zurück zum Zitat Venske SM, Goncalves RA, Delgado MR (2014) ADEMO/D: multiobjective optimization by an adaptive differential evolution algorithm. Neurocomputing 127:65–77CrossRef Venske SM, Goncalves RA, Delgado MR (2014) ADEMO/D: multiobjective optimization by an adaptive differential evolution algorithm. Neurocomputing 127:65–77CrossRef
209.
Zurück zum Zitat Villalobos-Arias M, Coello Coello CA, Hernández-Lerma O (2006) Asymptotic convergence of metaheuristics for multiobjective optimization problems. Soft Comput 10(11):1001–1005CrossRef Villalobos-Arias M, Coello Coello CA, Hernández-Lerma O (2006) Asymptotic convergence of metaheuristics for multiobjective optimization problems. Soft Comput 10(11):1001–1005CrossRef
210.
Zurück zum Zitat Wang J, Terpenny JP (2005) Interactive preference incorporation in evolutionary engineering design. In: Jin Y (ed) Knowledge incorporation in evolutionary computation. Springer, Berlin/Heidelberg, pp 525–543. ISBN:3-540-22902-7CrossRef Wang J, Terpenny JP (2005) Interactive preference incorporation in evolutionary engineering design. In: Jin Y (ed) Knowledge incorporation in evolutionary computation. Springer, Berlin/Heidelberg, pp 525–543. ISBN:3-540-22902-7CrossRef
211.
Zurück zum Zitat Wang X, Tang J, Yung K (2009) Optimization of the multi-objective dynamic cell formation problem using a scatter search approach. Int J Adv Manuf Technol 44(3–4):318–329CrossRef Wang X, Tang J, Yung K (2009) Optimization of the multi-objective dynamic cell formation problem using a scatter search approach. Int J Adv Manuf Technol 44(3–4):318–329CrossRef
212.
Zurück zum Zitat Woldesenbet YG, Tessema BG, Yen GG (2007) Constraint handling in multi-objective evolutionary optimization. In: 2007 IEEE congress on evolutionary computation (CEC’2007), Singapore. IEEE Press, pp 3077–3084CrossRef Woldesenbet YG, Tessema BG, Yen GG (2007) Constraint handling in multi-objective evolutionary optimization. In: 2007 IEEE congress on evolutionary computation (CEC’2007), Singapore. IEEE Press, pp 3077–3084CrossRef
213.
Zurück zum Zitat Won KS, Ray T (2004) Performance of kriging and cokriging based surrogate models within the unified framework for surrogate assisted optimization. In: 2004 congress on evolutionary computation (CEC’2004), Portland, vol 2. IEEE Service Center, pp 1577–1585 Won KS, Ray T (2004) Performance of kriging and cokriging based surrogate models within the unified framework for surrogate assisted optimization. In: 2004 congress on evolutionary computation (CEC’2004), Portland, vol 2. IEEE Service Center, pp 1577–1585
214.
Zurück zum Zitat Xu J, Li Z (2012) Multi-objective dynamic costruction site layout plannig in fuzzy random environment. Autom Constr 27:155–169CrossRef Xu J, Li Z (2012) Multi-objective dynamic costruction site layout plannig in fuzzy random environment. Autom Constr 27:155–169CrossRef
215.
Zurück zum Zitat Yong W, Zixing C (2005) A constrained optimization evolutionary algorithm based on multiobjective optimization techniques. In: 2005 IEEE congress on evolutionary computation (CEC’2005), Edinburgh, vol 2. IEEE Service Center, pp 1081–1087 Yong W, Zixing C (2005) A constrained optimization evolutionary algorithm based on multiobjective optimization techniques. In: 2005 IEEE congress on evolutionary computation (CEC’2005), Edinburgh, vol 2. IEEE Service Center, pp 1081–1087
216.
Zurück zum Zitat Zapotecas Martínez S, Coello Coello CA (2011) A multi-objective particle swarm optimizer based on decomposition. In: 2011 genetic and evolutionary computation conference (GECCO’2011), Dublin. ACM Press, pp 69–76 Zapotecas Martínez S, Coello Coello CA (2011) A multi-objective particle swarm optimizer based on decomposition. In: 2011 genetic and evolutionary computation conference (GECCO’2011), Dublin. ACM Press, pp 69–76
217.
Zurück zum Zitat Zapotecas Martínez S, Coello Coello CA (2013) Combining surrogate models and local search for dealing with expensive multi-objective optimization problems. In: 2013 IEEE congress on evolutionary computation (CEC’2013), Cancún. IEEE Press, pp 2572–2579. ISBN:978-1-4799-0454-9CrossRef Zapotecas Martínez S, Coello Coello CA (2013) Combining surrogate models and local search for dealing with expensive multi-objective optimization problems. In: 2013 IEEE congress on evolutionary computation (CEC’2013), Cancún. IEEE Press, pp 2572–2579. ISBN:978-1-4799-0454-9CrossRef
218.
Zurück zum Zitat Zavala GR, Nebro AJ, Luna F, Coello Coello CA (2014) A survey of multi-objective metaheuristics applied to structural optimization. Struct Multidiscip Optim 49(4):537–558MathSciNetCrossRef Zavala GR, Nebro AJ, Luna F, Coello Coello CA (2014) A survey of multi-objective metaheuristics applied to structural optimization. Struct Multidiscip Optim 49(4):537–558MathSciNetCrossRef
219.
Zurück zum Zitat Zeng SY, Kang LS, Ding LX (2004) An orthogonal multi-objective evolutionary algorithm for multi-objective optimization problems with constraints. Evol Comput 12(1):77–98. Spring Zeng SY, Kang LS, Ding LX (2004) An orthogonal multi-objective evolutionary algorithm for multi-objective optimization problems with constraints. Evol Comput 12(1):77–98. Spring
220.
Zurück zum Zitat Zhang D, Gao Z (2012) Forward kinematics, performance analysis, and multi-objective optimization of a bio-inspired parallel manipulator. Robot Comput Intregr Manuf 28(4): 484–492CrossRef Zhang D, Gao Z (2012) Forward kinematics, performance analysis, and multi-objective optimization of a bio-inspired parallel manipulator. Robot Comput Intregr Manuf 28(4): 484–492CrossRef
221.
Zurück zum Zitat Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731CrossRef Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731CrossRef
222.
Zurück zum Zitat Zhang Q, Liu W, Tsang E, Virginas B (2010) Expensive multiobjective optimization by MOEA/D with Gaussian process model. IEEE Trans Evol Comput 14(3):456–474CrossRef Zhang Q, Liu W, Tsang E, Virginas B (2010) Expensive multiobjective optimization by MOEA/D with Gaussian process model. IEEE Trans Evol Comput 14(3):456–474CrossRef
223.
Zurück zum Zitat Zheng Y-J, Chen S-Y (2013) Cooperative particle swarm optimization for multiobjective transportation planning. Appl Intell 39(1):202–216MathSciNetCrossRef Zheng Y-J, Chen S-Y (2013) Cooperative particle swarm optimization for multiobjective transportation planning. Appl Intell 39(1):202–216MathSciNetCrossRef
224.
Zurück zum Zitat Zhu J, Cai X, Pan P, Gu R (2014) Multi-objective structural optimization design of horizontal-axis wind turbine blades using the non-dominated sorting genetic algorithm II and finite element method. Energies 7(2):988–1002CrossRef Zhu J, Cai X, Pan P, Gu R (2014) Multi-objective structural optimization design of horizontal-axis wind turbine blades using the non-dominated sorting genetic algorithm II and finite element method. Energies 7(2):988–1002CrossRef
225.
226.
Zurück zum Zitat Žilinskas A (2014) A statistical model-based algorithm for ‘black-box’ multi-objective optimisation. Int J Syst Sci 45(1):82–93MathSciNetCrossRef Žilinskas A (2014) A statistical model-based algorithm for ‘black-box’ multi-objective optimisation. Int J Syst Sci 45(1):82–93MathSciNetCrossRef
227.
Zurück zum Zitat Žilinskas A, Fraga ES, Mackuté A (2006) Data analysis and visualisation for robust multi-criteria process optimisation. Comput Chem Eng 30:1061–1071CrossRef Žilinskas A, Fraga ES, Mackuté A (2006) Data analysis and visualisation for robust multi-criteria process optimisation. Comput Chem Eng 30:1061–1071CrossRef
228.
Zurück zum Zitat Žilinskas J, Goldengorin B, Pardalos PM (2015) Pareto-optimal front of cell formation problem in group technology. J Glob Optim 61:91–108MathSciNetCrossRef Žilinskas J, Goldengorin B, Pardalos PM (2015) Pareto-optimal front of cell formation problem in group technology. J Glob Optim 61:91–108MathSciNetCrossRef
229.
Zurück zum Zitat Zitzler E, Künzli S (2004) Indicator-based selection in multiobjective search. In: Yao X et al (ed) Parallel problem solving from nature – PPSN VIII, Birmingham. Lecture notes in computer science, vol 3242. Springer, pp 832–842 Zitzler E, Künzli S (2004) Indicator-based selection in multiobjective search. In: Yao X et al (ed) Parallel problem solving from nature – PPSN VIII, Birmingham. Lecture notes in computer science, vol 3242. Springer, pp 832–842
230.
Zurück zum Zitat Zitzler E, Deb K, Thiele L (1999) Comparison of multiobjective evolutionary algorithms on test functions of different difficulty. In: Wu AS (ed) Proceedings of the 1999 genetic and evolutionary computation conference. Workshop program, Orlando, pp 121–122 Zitzler E, Deb K, Thiele L (1999) Comparison of multiobjective evolutionary algorithms on test functions of different difficulty. In: Wu AS (ed) Proceedings of the 1999 genetic and evolutionary computation conference. Workshop program, Orlando, pp 121–122
231.
Zurück zum Zitat Zitzler E, Laumanns M, Thiele L (2002) SPEA2: improving the strength Pareto evolutionary algorithm. In: Giannakoglou K, Tsahalis D, Periaux J, Papailou P, Fogarty T (eds) EUROGEN 2001. Evolutionary methods for design, optimization and control with applications to industrial problems, Athens, pp 95–100 Zitzler E, Laumanns M, Thiele L (2002) SPEA2: improving the strength Pareto evolutionary algorithm. In: Giannakoglou K, Tsahalis D, Periaux J, Papailou P, Fogarty T (eds) EUROGEN 2001. Evolutionary methods for design, optimization and control with applications to industrial problems, Athens, pp 95–100
232.
Zurück zum Zitat Zitzler E, Thiele L, Laumanns M, Fonseca CM, da Fonseca VG (2003) Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evol Comput 7(2):117–132CrossRef Zitzler E, Thiele L, Laumanns M, Fonseca CM, da Fonseca VG (2003) Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evol Comput 7(2):117–132CrossRef
233.
Zurück zum Zitat Zitzler E, Laumanns M, Bleuler S (2004) A tutorial on evolutionary multiobjective optimization. In: Gandibleux X, Sevaux M, Sörensen K, T’kindt V (eds) Metaheuristics for multiobjective optimisation, Berlin. Lecture notes in economics and mathematical systems, vol 535. Springer, pp 3–37 Zitzler E, Laumanns M, Bleuler S (2004) A tutorial on evolutionary multiobjective optimization. In: Gandibleux X, Sevaux M, Sörensen K, T’kindt V (eds) Metaheuristics for multiobjective optimisation, Berlin. Lecture notes in economics and mathematical systems, vol 535. Springer, pp 3–37
Metadaten
Titel
Multi-objective Optimization
verfasst von
Carlos A. Coello Coello
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-07124-4_17