Multimodal Multiobjective Evolutionary Optimization With Dual Clustering in Decision and Objective Spaces

This article suggests a multimodal multiobjective evolutionary algorithm with dual clustering in decision and objective spaces. One clustering is run in decision space to gather nearby solutions, which will classify solutions into multiple local clusters. Nondominated solutions within each local clu...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 25; H. 1; S. 130 - 144
Hauptverfasser: Lin, Qiuzhen, Lin, Wu, Zhu, Zexuan, Gong, Maoguo, Li, Jianqiang, Coello, Carlos A. Coello
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Classification algorithms
Clustering
Clustering algorithms
Convergence
evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
multimodal optimization
multiobjective optimization
Multiple objective analysis
Optimization
Pareto optimization
Sociology
Statistics
ISSN:1089-778X, 1941-0026
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Zusammenfassung:This article suggests a multimodal multiobjective evolutionary algorithm with dual clustering in decision and objective spaces. One clustering is run in decision space to gather nearby solutions, which will classify solutions into multiple local clusters. Nondominated solutions within each local cluster are first selected to maintain local Pareto sets, and the remaining ones with good convergence in objective space are also selected, which will form a temporary population with more than N solutions (N is the population size). After that, a second clustering is run in objective space for this temporary population to get N final clusters with good diversity in objective space. Finally, a pruning process is repeatedly run on the above clusters until each cluster has only one solution, which removes the most crowded solution in decision space from the most crowded cluster in objective space each time. This way, the clustering in decision space can distinguish all Pareto sets and avoid the loss of local Pareto sets, while that in objective space can maintain diversity in objective space. When solving all the benchmark problems from the competition of multimodal multiobjective optimization in the IEEE Congress on Evolutionary Computation 2019, the experiments validate our advantages to maintain diversity in both objective and decision spaces.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2020.3008822