Skip to main content
Register
Springer Professional
SEARCH
MENU 1 2 3 4
main-content
Top

Hint

Swipe to navigate through the chapters of this book

Published in:
Adaptive and Multilevel Metaheuristics Read chapter Read first chapter

2018 | OriginalPaper | Chapter

7. Multi-objective Optimization

Author: Carlos A. Coello Coello

Published in: Handbook of Heuristics

Publisher: Springer International Publishing

Login to get access
share
SHARE

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.
previous chapter Multi-start Methods
next chapter Restart Strategies
Literature
1.
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.
Akay B (2013) Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms. J Glob Optim 57(2):415–445
3.
Alba E, Luque G, Nesmachnow S (2013) Parallel metaheuristics: recent advances and new trends. Int Trans Oper Res 20(1):1–48
4.
Angus D, Woodward C (2009) Multiple objective ant colony optimisation. Swarm Intell 3(1):69–85
5.
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.
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.
Bader J, Zitzler E (2011) HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol Comput 19(1):45–76. Spring
8.
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.
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.
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.
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.
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.
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.
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.
Beausoleil RP (2006) “MOSS” multiobjective scatter search applied to non-linear multiple criteria optimization. Eur J Oper Res 169(2):426–449
16.
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.
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.
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.
Beume N, Naujoks B, Emmerich M (2007) SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur J Oper Res 181(3):1653–1669
20.
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.
Branke J (2002) Evolutionary optimization in dynamic environments. Kluwer Academic Publishers, Boston. ISBN:0-7923-7631-5
22.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Chang Y-C (2012) Multi-objective optimal SVC installation for power system loading margin improvement. IEEE Trans Power Syst 27(2):984–992
33.
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.
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.
Coello Coello CA (2000) Constraint-handling using an evolutionary multiobjective optimization technique. Civ Eng Environ Syst 17:319–346
36.
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.
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.
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.
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.
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.
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.
Collette Y, Siarry P (2003) Multiobjective optimization. Principles and case studies. Springer Berlin, Germany. ISBN:3-540-40182-2
43.
Corne D, Glover F, Dorigo M (eds) (1999) New ideas in optimization. McGraw-Hill, Berkshire. ISBN:007-709506-5
44.
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.
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.
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.
Cvetković D, Parmee IC (2002) Preferences and their application in evolutionary multiobjective optimisation. IEEE Trans Evol Comput 6(1):42–57
48.
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.
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.
Dasgupta D (ed) (1999) Artificial immune systems and their applications. Springer, Berlin
51.
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.
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester. ISBN:0-471-87339-X
53.
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.
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.
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.
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.
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.
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.
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.
Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, Cambridge. ISBN:0-262-04219-3
61.
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.
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.
Edgeworth FY (1881) Mathematical psychics. P. Keagan, London
64.
Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin. ISBN:3-540-40184-9
65.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Fogel LJ (1966) Artificial intelligence through simulated evolution. John Wiley, New York
75.
Fogel DB (1995) Evolutionary computation. Toward a new philosophy of machine intelligence. The Institute of Electrical and Electronic Engineers, New York
76.
Fogel LJ (1999) Artificial intelligence through simulated evolution. Forty years of evolutionary programming. Wiley, New York
77.
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.
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.
Freschi F, Repetto M (2006) VIS: an artificial immune network for multi-objective optimization. Eng Optim 38(8):975–996
80.
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.
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.
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.
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.
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.
Gen M, Cheng R (2000) Genetic algorithms and engineering optimization. Wiley series in engineering design and automation. Wiley, New York
86.
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.
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.
Glover F, Kochenberger GA (eds) (2003) Handbook of metaheuristics. Kluwer Academic Publishers, Boston. ISBN:1-4020-7263-5
89.
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Reading
90.
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.
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.
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.
Hajela P, Lin CY (1992) Genetic search strategies in multicriterion optimal design. Struct Optim 4:99–107
94.
Hansen MP (1998) Metaheuristics for multiple objective combinatorial optimization. PhD thesis, Institute of Mathematical Modelling, Technical University of Denmark
95.
Hansen MP (2000) Tabu search for multiobjective combinatorial optimization: TAMOCO. Control Cybern 29(3):799–818
96.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Hüscken M, Jin Y, Sendhoff B (2005) Structure optimization of neural networks for aerodynamic optimization. Soft Comput 9(1):21–28
109.
Ibaraki T, Nonobe K, Yagiura M (eds) (2005) Metaheuristics. Progress as real problem solvers. Springer, New York. ISBN:978-0-387-25382-4
110.
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.
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.
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.
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.
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.
Kelaiaia R, Zaatri A, Company O (2012) Multiobjective optimization of 6-dof UPS parallel manipulators. Adv Robot 26(16):1885–1913
116.
Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann Publishers, San Francisco
117.
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.
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.
Knowles JD, Corne DW (2000) Approximating the nondominated front using the pareto archived evolution strategy. Evol Comput 8(2):149–172
120.
Knowles J, Corne D (2003) Properties of an adaptive archiving algorithm for storing nondominated vectors. IEEE Trans Evol Comput 7(2):100–116
121.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Luh G-C, Chueh C-H, Liu W-W (2003) MOIA: multi-objective immune algorithm. Eng Optim 35(2):143–164
131.
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.
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.
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.
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.
Miettinen KM (1999) Nonlinear multiobjective optimization. Kluwer Academic Publishers, Boston
136.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Ning X, Lam KC (2013) Cost-safety trade-off in unequal-area construction site layout planning. Autom Constr 32:96–103
146.
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.
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.
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.
Pacheco J, Marti R (2006) Tabu search for a multi-objective routing problem. J Oper Res Soc 57(1):29–37
150.
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.
Pardalos PM, Žilinskas A, Žilinskas J (2017) Non-convex multi-objective optimization. Springer, Cham. ISBN:978-3-319-61005-4
152.
Pareto V (1896) Cours D’Economie Politique, vol I and II. F. Rouge, Lausanne
153.
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.
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.
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.
Pierret S (1999) Turbomachinery blade design using a Navier-Stokes solver and artificial neural network. ASME J Turbomach 121(3):326–332
157.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Rosenberg R (1967) Simulation of genetic populations with biochemical properties. PhD thesis, Department of Communication Sciences, University of Michigan, Ann Arbor
174.
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.
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.
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.
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.
Schaffer JD (1984) Multiple objective optimization with vector evaluated genetic algorithms. PhD thesis, Vanderbilt University, Nashville
179.
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.
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.
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.
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.
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.
Schwefel H-P (1965) Kybernetische evolution als strategie der experimentellen forschung in der strömungstechnik. Dipl.-Ing. thesis (in German)
185.
Schwefel H-P (1981) Numerical optimization of computer models. Wiley, Chichester
186.
Sharifi S, Massoudieh A (2012) A novel hybrid mechanistic-data-driven model identification framework using NSGA-II. J Hydroinf 14(3):697–715
187.
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.
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.
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.
Smith KI (2006) A study of simulated annealing techniques for multi-objective optimisation. PhD thesis, University of Exeter
191.
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.
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.
Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248. Fall
194.
Suman B, Kumar P (2006) A survey of simulated annealing as a tool for single and multiobjective optimization. J Oper Res Soc 57(10):1143–1160 CrossRef
195.
Surry PD, Radcliffe NJ (1997) The COMOGA method: constrained optimisation by multiobjective genetic algorithms. Control Cybern 26(3):391–412 MATH
196.
Sweetapple C, Fu G, Butler D (2014) Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions. Water Res 55:52–62 CrossRef
197.
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.
Talbi E-G (ed) (2009) Metaheuristics. From design to implementation. Wiley, New Jersey. ISBN:978-0-470-27858-1 MATH
199.
Talukder AKMKA, Kirley M, Buyya R (2009) Multiobjective differential evolution for scheduling workflow applications on global Grids. Concurrency Comput-Pract Exp 21(13):1742–1756 CrossRef
200.
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):565–588 CrossRef
201.
Tan KC, Khor EF, Lee TH (2005) Multiobjective evolutionary algorithms and applications. Springer, London. ISBN:1-85233-836-9 MATH
202.
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.
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.
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.
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.
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.
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:978-1-4799-0454-9 CrossRef
208.
Venske SM, Goncalves RA, Delgado MR (2014) ADEMO/D: multiobjective optimization by an adaptive differential evolution algorithm. Neurocomputing 127:65–77 CrossRef
209.
Villalobos-Arias M, Coello Coello CA, Hernández-Lerma O (2006) Asymptotic convergence of metaheuristics for multiobjective optimization problems. Soft Comput 10(11):1001–1005 CrossRef
210.
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:3-540-22902-7 CrossRef
211.
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):318–329 CrossRef
212.
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 3077–3084 CrossRef
213.
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.
Xu J, Li Z (2012) Multi-objective dynamic costruction site layout plannig in fuzzy random environment. Autom Constr 27:155–169 CrossRef
215.
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.
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.
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:978-1-4799-0454-9 CrossRef
218.
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):537–558 MathSciNetCrossRef
219.
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.
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): 484–492 CrossRef
221.
Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731 CrossRef
222.
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):456–474 CrossRef
223.
Zheng Y-J, Chen S-Y (2013) Cooperative particle swarm optimization for multiobjective transportation planning. Appl Intell 39(1):202–216 MathSciNetCrossRef
224.
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):988–1002 CrossRef
225.
226.
Žilinskas A (2014) A statistical model-based algorithm for ‘black-box’ multi-objective optimisation. Int J Syst Sci 45(1):82–93 MathSciNetCrossRef
227.
Žilinskas A, Fraga ES, Mackuté A (2006) Data analysis and visualisation for robust multi-criteria process optimisation. Comput Chem Eng 30:1061–1071 CrossRef
228.
Žilinskas J, Goldengorin B, Pardalos PM (2015) Pareto-optimal front of cell formation problem in group technology. J Glob Optim 61:91–108 MathSciNetCrossRef
229.
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.
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.
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.
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):117–132 CrossRef
233.
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
Metadata
Title
Multi-objective Optimization
Author
Carlos A. Coello Coello
Copyright Year
2018
Book
Handbook of Heuristics

Print ISBN: 978-3-319-07123-7

Electronic ISBN: 978-3-319-07124-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-07124-4_17

Premium Partner

Neuer Inhalt
    Image Credits

    Version: 0.2067.0