Top

Memetic Computing

Published in:

01-06-2015 | Regular Research Paper

Many-objective optimization with corner-based search

Authors: Hélio Freire, P. B. de Moura Oliveira, E. J. Solteiro Pires, Maximino Bessa

Published in: Memetic Computing | Issue 2/2015

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The performance of multi-objective evolutionary algorithms can severely deteriorate when applied to problems with 4 or more objectives, called many-objective problems. For Pareto dominance based techniques, available information about some optimal solutions can be used to improve their performance. This is the case of corner solutions. This work considers the behaviour of three multi-objective algorithms [Non-dominated sorting genetic algorithm (NSGA-II), Speed-constrained multi-objective particle swarm optimization (SMPSO) and generalized differential evolution (GDE3)] when corner solutions are inserted into the population at different evolutionary stages. The problem of finding corner solutions is addressed by proposing a new algorithm based in multi-objective particle swarm optimization (MOPSO). Results concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5) and respective analysis are presented.

previous article An effective multiobjective approach for hard partitional clustering

next article A novel two-level particle swarm optimization approach for efficient multiple sequence alignment

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Adra SF, Fleming PJ (2011) Diversity management in evolutionary many-objective optimization. IEEE Trans Evolut Comput 15(2):183–195 ISSN 1089–778XCrossRef

Bansal JC, Singh PK, Saraswat Mukesh, Verma Abhishek, Jadon Shimpi Singh, Abraham Ajith (2011) Inertia weight strategies in particle swarm optimization. In NaBIC, pp 633–640. IEEE, ISBN 978-1-4577-1122-0

Bechikh S, Ben Said L, Ghedira K (2010) Estimating nadir point in multi-objective optimization using mobile reference points. In: Evolutionary computation (CEC), 2010 IEEE congress on, pp 1–9

Bechikh Slim, Said Lamjed Ben, Ghédira Khaled (2010) Searching for knee regions in multi-objective optimization using mobile reference points. In SAC, pp 1118–1125

Bentley PJ, Wakefield JP (1998) Finding acceptable solutions in the Pareto-optimal range using multiobjective genetic algorithms. In: Chawdhry PK, Roy R, Pant RK (eds) Soft computing in engineering design and manufacturing. Springer, London, pp 231–240

Branke J, Deb K, Dierolf H, Osswald M (2004) Finding knees in multi-objective optimization. In PPSN, pp 722–731

Brockhoff Dimo, Zitzler Eckart (2009) Objective reduction in evolutionary multiobjective optimization: Theory and applications. Evolution Comput 17(2):135–166CrossRef

Chaudhuri S, Deb K (2010) An interactive evolutionary multi-objective optimization and decision making procedure. Appl Soft Comput 10(2):496–511 ISSN 15684946CrossRef

Corne D, Knowles J (2007) Techniques for highly multiobjective optimisation : some nondominated points are better than others. pp 773–780

10.

Dasgupta D, Hernandez G, Romero A, Garrett D, Kaushal A, Simien J (2009) On the use of informed initialization and extreme solutions sub-population in multi-objective evolutionary algorithms. In: Computational intelligence in miulti-criteria decision-making, 2009. MCDM ’09. IEEE symposium on, pp 58–65

11.

Deb K, Jain H (2012) Handling many-objective problems using an improved NSGA-II procedure. In: Evolutionary computation (CEC), 2012 IEEE congress on, pp 1–8

12.

Deb K, Miettinen K (2010) Nadir point estimation using evolutionary approaches: better accuracy and computational speed through focused search. In: Multiple criteria decision making for sustainable energy and transportation systems, vol 634, pp 339–354. Springer, Berlin. ISBN 978-3-642-04044-3

13.

Deb K, Thiele L, Laumanns M, Zitzler E (2005) Scalable test problems for evolutionary multiobjective optimization . In: Abraham A, Jain L, Goldberg R (eds) Evolutionary multiobjective optimization. Advanced information and knowledge processing. Springer, London, pp 105–145

14.

Deb K, Agrawal S, Pratap A, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolution Comput 6(2):182–197CrossRef

15.

Deb K, Chaudhuri S, Miettinen K (2006) Towards estimating nadir objective vector using evolutionary approaches. In: Proceedings of the 8th annual conference on genetic and evolutionary computation, GECCO ’06, pp 643–650, New York, NY, USA, ACM. ISBN 1-59593-186-4

16.

Deb K, Miettinen K, Chaudhuri S (2010) Toward an estimation of nadir objective vector using a hybrid of evolutionary and local search approaches. IEEE Trans Evolution Comput 14(6):821–841CrossRef

17.

di Pierro F, Khu S-T, Savic DA (2007) An investigation on preference order ranking scheme for multiobjective evolutionary optimization. IEEE Trans Evolution Comput 11(1):17–45CrossRef

18.

Durillo JJ, Nebro AJ (2011) Jmetal:a java framework for multi-objective optimization. Adv Eng Softw 42:760–771 ISSN 0965–9978CrossRef

19.

Fleming PJ, Purshouse RC, Lygoe RJ (2005) Many-objective optimization: an engineering design perspective. Design 3410(6):14–32 ISSN 15393755

20.

Freire H, de Moura Oliveira PB, Solteiro Pires EJ, Lopes António M (2011) Maximin MOPSO design of parallel robotic manipulators. In: Emilio et al. Corchado, editor, SOCO, volu 87 of advances in soft computing, pp 339–347. Springer, Berlin. ISBN 978-3-642-19643-0

21.

Freire H, Moura Oliveira PB, Solteiro Pires EJ, Bessa M (2014) Corner based many-objective optimization. In: German T, Fernando EB, Otero, Antonio DM (eds) Nature inspired cooperative strategies for optimization (NICSO 2013), volume 512 of studies in computational intelligence, pp 125–139, Springer International Publishing. ISBN 978-3-319-01691-7

22.

Garza-Fabre M, Toscano-Pulido G, Coello Coello CA, Rodriguez-Tello E (2011) Effective ranking speciation many-objective optimization

23.

Gutierrez AL, Lanza M, Barriuso I, Valle L, Domingo M, Perez JR, Basterrechea J (2011) Comparison of different PSO initialization techniques for high dimensional search space problems: a test with FSS and antenna arrays

24.

Hadka D, Reed PM, Simpson TW (2012) Diagnostic assessment of the borg MOEA for many-objective product family design problems. In: Evolutionary computation (CEC), 2012 IEEE congress on, pp 1–10

25.

Hughes Evan J (2006) Radar waveform optimisation as a many-objective application benchmark. In EMO, pp 700–714

26.

Ishibuchi H, Tsukamoto N, Nojima Y (2008) Evolutionary many-objective optimization: a short review. 2008 IEEE Congress on evolutionary computation IEEE world congress on computational intelligence (March), pp 2419–2426

27.

Jaimes Antonio L, Coello Coello CA, Chakraborty D (2008) Objective reduction using a feature selection technique. In GECCO, pp 673–680

28.

Jaimes Antonio L, Coello C, Oyama A, Fujii K (2013) An alternative preference relation to deal with many-objective optimization problems. In EMO, pp 291–306

29.

Kasprzyk Joseph R, Reed Patrick M, Characklis Gregory W, Kirsch Brian R (2012) Many-objective de novo water supply portfolio planning under deep uncertainty. Environ Model Softw, 34(0): 87–104, ISSN 1364–8152. Emulation techniques for the reduction and sensitivity analysis of complex environmental models

30.

Kennedy J, Eberhart R (1995) Particle swarm optimization. In Neural Networks, 1995. In: Proceedings., IEEE international conference on, vol 4, pp 1942–1948, 1995. ISBN 0-7803-2768-3. doi:10.1109/icnn.1995.488968

31.

Köppen M, Yoshida K (2007) Substitute distance assignments in NSGA-II for handling many-objective optimization problems. In EMO07, pp 727–741

32.

Kukkonen S, Lampinen J (2005) GDE3: the third evolution step of generalized differential evolution. In Evolutionary computation, 2005. The 2005 IEEE congress on, vol 1, pp 443–450

33.

Latiff IA, Tokhi MO (2009) Fast convergence strategy for particle swarm optimization using spread factor. In Evolutionary computation, 2009. CEC ’09. IEEE congress on, pp 2693–2700, May 2009

34.

Li G, Hu H (2014) Risk design optimization using many-objective evolutionary algorithm with application to performance-based wind engineering of tall buildings. Struct Safety 48(0):1–14 ISSN 0167–4730CrossRef

35.

Lygoe Robert J, Cary M, Fleming Peter J (2013) A real-world application of a many-objective optimisation complexity reduction process. In EMO, pp 641–655

36.

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: Computational intelligence in miulti-criteria decision-making, 2009. MCDM ’09. IEEE symposium on, pp 66–73

37.

Obayashi S, Sasaki D (2003) Visualization and data mining of pareto solutions using self-organizing map. In: Proceedings of the 2nd international conference on evolutionary multi-criterion optimization, EMO’03, pp796–809, Berlin, Heidelberg, Springer-Verlag. ISBN 3-540-01869-7

38.

Pryke A, Mostaghim S, Nazemi A(2006) Heatmap visualization of population based multi objective algorithms. In: Shigeru O, Kalyanmoy D, Carlo P, Tomoyuki H Tadahiko M (eds) EMO, volume 4403 of lecture notes in computer science, pp 361–375. Springer, 2006. ISBN 3-540-70927-4

39.

Reed PM, Hadka D, Herman JD, Kasprzyk JR, Kollat JB (2013) Evolutionary multiobjective optimization in water resources: the past, present, and future. Adv Water Resour, 51(0):438–456, ISSN 0309–1708. 35th Year Anniversary Issue

40.

Reyes-Sierra M, Coello Coello CA (2006) Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intel Res 2(3):287–308MathSciNet

41.

Saxena DK, Deb K (2007) Non-linear dimensionality reduction procedures for certain large-dimensional multi-objective optimization problems: employing correntropy and a novel maximum variance unfolding. In: Proceedings of the 4th international conference on evolutionary multi-criterion optimization, EMO’07, pp 772–787, Berlin, Heidelberg, Springer-Verlag. ISBN 978-3-540-70927-5

42.

Saxena DK, Duro JA, Tiwari A, Deb K, Zhang Q (2013) Objective Reduction in many-objective optimization: linear and nonlinear algorithms. IEEE Tran Evolut Comput 17(1):77–99 ISSN 1089–778XCrossRef

43.

Schott Jason R (1995) Fault tolerant design using single and multicriteria genetic algorithm optimization. Master’s thesis, MIT, May 1995

44.

Siegmund F, Bernedixen J, Pehrsson L, Ng Amos HC, Deb K (2012) Reference point-based evolutionary multi-objective optimization for industrial systems simulation. In: Proceedings of the winter simulation conference, WSC ’12, pp 130:1–130:11. Winter Simulation Conference

45.

Sierra Margarita R, Coello Coello CA (2005) Improving pso-based multi-objective optimization using crowding, mutation and \(\epsilon \)-dominance. In: Proceedings of the third international conference on evolutionary multi-criterion optimization, EMO’05, pp 505–519. Springer-Verlag, 978–3, ISBN 3-540-24983-4-540-24983-2

46.

Singh HK, Isaacs A, Ray T (2011) A pareto corner search evolutionary algorithm and dimensionality reduction in many-objective optimization problems. IEEE Trans Evolut Comput 15(4):539–556CrossRef

47.

Sinha A, Saxena DK, Deb K, Tiwari A (2013) Using objective reduction and interactive procedure to handle many-objective optimization problems. Appl Soft Comput 13(1):415–427 ISSN 15684946CrossRef

48.

Wierzbicki AP, Szczepanski M (2003) Application of multiple criterion evolutionary algorithm to vector optimization, decision support and reference-point approaches. Telecommun Inf Technol 3(3):16–33

49.

Van Veldhuizen David A, Lamont Gary B (1998) Evolutionary computation and convergence to a pareto front. In Stanford University, pp 221–228. Morgan Kaufmann

50.

Walker DJ, Everson RM, Fieldsend JE (2010) Visualisation and ordering of many-objective populations

51.

Wegman Edward J (1990) Hyperdimensional data analysis using parallel coordinates. J Am Stat Assoc 85(411):664–675 ISSN 01621459CrossRef

Title: Many-objective optimization with corner-based search
Authors: Hélio Freire
P. B. de Moura Oliveira
E. J. Solteiro Pires
Maximino Bessa
Publication date: 01-06-2015
Publisher: Springer Berlin Heidelberg
Published in: Memetic Computing / Issue 2/2015
Print ISSN: 1865-9284
Electronic ISSN: 1865-9292
DOI: https://doi.org/10.1007/s12293-015-0151-4

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 2/2015

An effective multiobjective approach for hard partitional clustering

A novel two-level particle swarm optimization approach for efficient multiple sequence alignment

Decentralized and dynamic group formation of reconfigurable agents

Editorial

Quantum-Inspired Evolutionary Algorithm for difficult knapsack problems

Premium Partner