research-article

Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems

Authors:

Hisao Ishibuchi,

Noritaka Tsukamoto,

Yasuhiro Hitotsuyanagi,

Yusuke NojimaAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 649 - 656

https://doi.org/10.1145/1389095.1389225

Published: 12 July 2008 Publication History

Abstract

Recently a number of approaches have been proposed to improve the scalability of evolutionary multiobjective optimization (EMO) algorithms to many-objective problems. In this paper, we examine the effectiveness of those approaches through computational experiments on multiobjective knapsack problems with two, four, six, and eight objectives. First we briefly review related studies on evolutionary many-objective optimization. Next we explain why Pareto dominance-based EMO algorithms do not work well on many-objective optimization problems. Then we explain various scalability improvement approaches. We examine their effects on the performance of NSGA-II through computational experiments. Experimental results clearly show that the diversity of solutions is decreased by most scalability improvement approaches while the convergence of solutions to the Pareto front is improved. Finally we conclude this paper by pointing out future research directions.

References

[1]

Abraham, A., Jain, L. C., and Goldberg, R. (eds.) Evolutionary Multiobjective Optimization: Theoretical Advances and Applications. Springer, Berlin (2005).]]

Digital Library

[2]

Branke, J., Kaußler, T., and Schmeck, H. Guidance in evolutionary multi-objective optimization. Advances in Engineering Software 32, 6 (2001) 499--507.]]

[3]

Brockhoff, D., and Zitzler, E. Are all objectives necessary? On dimensionality reduction in evolutionary multiobjective optimization. Lecture Notes in Computer Science 4193: Parallel Problem Solving from Nature - PPSN IX, Springer, Berlin (2006) 533--542.]]

Digital Library

[4]

Brockhoff, D., and Zitzler, E. Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods. Proc. of 2007 IEEE Congress on Evolutionary Computation (2007) 2086--2093.]]

[5]

Coello, C. A. C., and Lamont, G. B. Applications of Multi-Objective Evolutionary Algorithms. World Scientific, Singapore (2004).]]

[6]

Coello, C. A. C., van Veldhuizen, D. A., and Lamont, G. B. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer, Boston (2002).]]

Digital Library

[7]

Corne, D., and Knowles, J. Techniques for highly multiobjective optimization: Some non-dominated points are better than others. Proc. of 2007 Genetic and Evolutionary Computation Conference (2007) 773--780.]]

Digital Library

[8]

Deb, K. Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001).]]

Digital Library

[9]

Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6, 2 (2002) 182--197.]]

Digital Library

[10]

Deb, K., and Saxena, D. K. On finding Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. KanGAL Report, No. 2005011, Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology Kanpur (2005).]]

[11]

Deb, K., and Saxena, K. Searching for Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. Proc. of 2006 IEEE Congress on Evolutionary Computation (2006) 3353--3360.]]

[12]

Deb, K., and Sundar, J. Preference point based multi-objective optimization using evolutionary algorithms. Proc. of 2006 Genetic and Evolutionary Computation Conference (2006) 635--642.]]

Digital Library

[13]

Drechsler, N., Drechsler, R., and Becker, B. Multi-objective optimization based on relation favour. Lecture Notes in Computer Science 1993: Evolutionary Multi-Criterion Optimization - EMO 2001, Springer, Berlin (2001) 154--166.]]

Digital Library

[14]

Fleming, P. J., Purshouse, R. C., and Lygoe, R. J. Many-objective optimization: An engineering design perspective. Lecture Notes in Computer Science 3410: Evolutionary Multi-Criterion Optimization - EMO 2005, Springer, Berlin (2005) 14--32.]]

Digital Library

[15]

Hughes, E. J. Evolutionary many-objective optimization: Many once or one many?. Proc. of 2005 IEEE Congress on Evolutionary Computation (2005) 222--227.]]

[16]

Hughes, E. J. MSOPS-II: A general-purpose many-objective optimizer. Proc. of 2007 IEEE Congress on Evolutionary Computation (2007) 3944--3951.]]

[17]

Ikeda, K., Kita, H., and Kobayashi, S. Failure of Pareto-based MOEAs: Does non-dominated really mean near to optimal?. Proc. of 2001 IEEE Congress on Evolutionary Computation (2001) 957--962.]]

[18]

Ishibuchi, H., Doi, T., and Nojima, Y. Incorporation of scalarizing fitness functions into evolutionary multiobjective optimization algorithms. Lecture Notes in Computer Science 4193: Parallel Problem Solving from Nature - PPSN IX, Springer, Berlin (2006) 493--502.]]

Digital Library

[19]

Ishibuchi, H., and Nojima, Y. Optimization of scalarizing functions through evolutionary multiobjective optimization. Lecture Notes in Computer Science 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, Springer, Berlin (2007) 51--65.]]

Digital Library

[20]

Ishibuchi, H., Tsukamoto, N., and Nojima, Y. Iterative approach to indicator-based multiobjective optimization. Proc. of 2007 IEEE Congress on Evolutionary Computation (2007) 3697--3704.]]

[21]

Ishibuchi, H., Tsukamoto, N., and Nojima, Y. Evolutionary many-objective optimization: A short review. Proc. of 2008 IEEE Congress on Evolutionary Computation (in press).]]

[22]

Jaszkiewicz, A. On the performance of multiple-objective genetic local search on the 0/1 knapsack problem - A comparative experiment. IEEE Trans. on Evolutionary Computation 6, 4 (2002) 402--412.]]

Digital Library

[23]

Jaszkiewicz, A. On the computational efficiency of multiple objective metaheuristics: The knapsack problem case study. European Journal of Operational Research 158, 2 (2004) 418--433.]]

[24]

Khara, V., Yao, X., and Deb, K. Performance scaling of multi-objective evolutionary algorithms. Lecture Notes in Computer Science 2632: Evolutionary Multi-Criterion Optimization - EMO 2003, Springer, Berlin (2004) 367--390.]]

Digital Library

[25]

Köppen, M. and Yoshida, K. Substitute distance assignments in NSGA-II for handling many-objective optimization problems. Lecture Notes in Computer Science 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, Springer, Berlin (2007) 727--741.]]

Digital Library

[26]

Kukkonen, S., and Lampinen, J. Ranking-dominance and many-objective optimization. Proc. of 2007 IEEE Congress on Evolutionary Computation (2007) 3983--3990.]]

[27]

Purshouse, R. C., and Fleming, P. J. Evolutionary many-objective optimization: An exploratory analysis. Proc. of 2003 IEEE Congress on Evolutionary Computation (2003) 2066--2073.]]

[28]

Sato, H., Aguirre, H. E., and Tanaka, K. Controlling dominance area of solutions and its impact on the performance of MOEAs. Lecture Notes in Computer Science 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, Springer, Berlin (2007) 5--20.]]

Digital Library

[29]

Sülflow, A., Drechsler, N., and Drechsler, R. Robust multi-objective optimization in high dimensional spaces. Lecture Notes in Computer Science 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, Springer, Berlin (2007) 715--726.]]

Digital Library

[30]

Thiele, L., Miettinen, K., Korhonen, P. J., and Molina, J. A preference-based interactive evolutionary algorithm for multiobjective optimization. Helsinki School of Economics, Working Paper (2007) W-412.]]

[31]

Wagner, T., Beume, N., and Naujoks, B. Pareto-, aggregation-, and indicator-based methods in many-objective optimization. Lecture Notes in Computer Science 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, Springer, Berlin (2007) 742--756.]]

Digital Library

[32]

Zhang, Q., and Li, H. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. on Evolutionary Computation 11, 6 (2007) 712--731.]]

Digital Library

[33]

Zitzler, E., and Thiele, L. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Trans. on Evolutionary Computation 3, 4 (1999) 257--271.]]

Digital Library

Cited By

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https://doi.org/10.1007/s10586-024-04675-1
Dean JCheney NSilva SPaquete L(2023)Many-objective Optimization via Voting for ElitesProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3590693(131-134)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3590693
Moqa RYounas IBashir M(2022)Assessing effectiveness of many-objective evolutionary algorithms for selection of tag SNPsPLOS ONE10.1371/journal.pone.027856017:12(e0278560)Online publication date: 8-Dec-2022
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Index Terms

Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

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Published: 12 July 2008

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Dean JCheney NSilva SPaquete L(2023)Many-objective Optimization via Voting for ElitesProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3590693(131-134)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3590693
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https://doi.org/10.1371/journal.pone.0278560
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