A two-stage evolutionary algorithm based on three indicators for constrained multi-objective optimization

One of the key issues in solving constrained multi-objective optimization problems (CMOPs) is balancing the three indicators of convergence, diversity, and feasibility. We believe at different stages of the evolution, different indicators should be emphasized. This paper proposes a two-stage constra...

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Veröffentlicht in:	Expert systems with applications Jg. 195; S. 116499
Hauptverfasser:	Dong, Jun, Gong, Wenyin, Ming, Fei, Wang, Ling
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 01.06.2022 Elsevier BV
Schlagworte:	Benchmarks Constrained multi-objective optimization Convergence Diversity Evolutionary algorithm Evolutionary algorithms Feasibility Genetic algorithms Indicators Multiple objective analysis Pareto optimization Performance evaluation Two-stage Constrained multi-objective optimization Feasibility Two-stage Evolutionary algorithm Diversity Convergence
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
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