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Pareto dominance-based MOEAs on problems with difficult pareto set topologies

Authors:

Kiyoshi TanakaAuthors Info & Claims

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 189 - 190

https://doi.org/10.1145/3205651.3205746

Published: 06 July 2018 Publication History

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Abstract

Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems (MOPs), understanding their working principles is still open to research. One of the most popular and successful MOEA approaches is based on Pareto dominance and its relaxed version, Pareto ϵ-dominance. However, such approaches have not been sufficiently studied in problems of increased complexity. In this work, we study the effects of the working mechanisms of the various components of these algorithms on test problems with difficult Pareto set topologies. We focus on separable unimodal and multimodal functions with 2, 3, and 4 objectives, all having difficult Pareto set topologies. Our experimental study provides some interesting and useful insights to understand better Pareto dominance-based MOEAs.

References

[1]

Hernán Aguirre, Akira Oyama, and Kiyoshi Tanaka. 2013. Adaptive ϵ-Sampling and ϵ-Hood for Evolutionary Many-Objective Optimization. In Evolutionary Multi-Criterion Optimization. Springer Berlin Heidelberg, Berlin, Heidelberg, 322--336.

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[2]

Hernán Aguirre, Yuki Yazawa, Akira Oyama, and Kiyoshi Tanaka. 2014. Extending AεSεH from Many-objective to Multi-objective Optimization. In Proceedings of 10th International Conference on Simulated Evolution and Learning. 239--250.

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[3]

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

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[4]

Saúl Zapotecas Martínez, Carlos A. Coello Coello, Hernán Aguirre, and Kiyoshi Tanaka. 2018. ZCAT: A New Set Of Scalable Multi-Objective Test Problems. Technical Report. Universidad Auntónoma Metropolitana, https://sites.google.com/view/szapotecas/zcat

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Kalita KRamesh JCepova LPandya SJangir PAbualigah L(2024)Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problemsScientific Reports10.1038/s41598-024-52083-714:1Online publication date: 20-Jan-2024
https://doi.org/10.1038/s41598-024-52083-7
Yacoubi SManita GChhabra AKorbaa OMirjalili S(2023)A multi-objective Chaos Game Optimization algorithm based on decomposition and random learning mechanisms for numerical optimizationApplied Soft Computing10.1016/j.asoc.2023.110525144(110525)Online publication date: Sep-2023
https://doi.org/10.1016/j.asoc.2023.110525
Dash SDey SAugustine ADhar RPidanic JNemec ZTrivedi G(2019)RiverOpt: A Multiobjective Optimization Framework Based on Modified River Formation Dynamics Heuristic2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID)10.1109/VLSID.2019.00059(233-238)Online publication date: Jan-2019
https://doi.org/10.1109/VLSID.2019.00059
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Index Terms

Pareto dominance-based MOEAs on problems with difficult pareto set topologies
1. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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Published In

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2018

1968 pages

ISBN:9781450357647

DOI:10.1145/3205651

Editor:
Hernan Aguirre
Shinshu University
,
General Chair:
Keiki Takadama
The University of Electro-Communications

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 06 July 2018

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GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

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Kalita KRamesh JCepova LPandya SJangir PAbualigah L(2024)Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problemsScientific Reports10.1038/s41598-024-52083-714:1Online publication date: 20-Jan-2024
https://doi.org/10.1038/s41598-024-52083-7
Yacoubi SManita GChhabra AKorbaa OMirjalili S(2023)A multi-objective Chaos Game Optimization algorithm based on decomposition and random learning mechanisms for numerical optimizationApplied Soft Computing10.1016/j.asoc.2023.110525144(110525)Online publication date: Sep-2023
https://doi.org/10.1016/j.asoc.2023.110525
Dash SDey SAugustine ADhar RPidanic JNemec ZTrivedi G(2019)RiverOpt: A Multiobjective Optimization Framework Based on Modified River Formation Dynamics Heuristic2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID)10.1109/VLSID.2019.00059(233-238)Online publication date: Jan-2019
https://doi.org/10.1109/VLSID.2019.00059
Marca YAguirre HMartinez SLiefooghe ADerbel BVerel STanaka K(2019)Approximating Pareto Set Topology by Cubic Interpolation on Bi-objective ProblemsEvolutionary Multi-Criterion Optimization10.1007/978-3-030-12598-1_31(386-398)Online publication date: 3-Feb-2019
https://doi.org/10.1007/978-3-030-12598-1_31

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A numerical method for constructing the Pareto front of multi-objective optimization problems

An improved marine predator algorithm based on epsilon dominance and Pareto archive for multi-objective optimization

Free lunches in pareto coevolution

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