Multiple Populations for Multiple Objectives Framework with Bias Sorting for Many-objective Optimization

The convergence and diversity enhancement of multiobjective evolutionary algorithms (MOEAs) to efficiently solve many-objective optimization problems (MaOPs) is an active topic in evolutionary computation. By considering the advantages of the multiple populations for multiple objectives (MPMO) frame...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 27; H. 5; S. 1
Hauptverfasser:	Yang, Qi-Te, Zhan, Zhi-Hui, Kwong, Sam, Zhang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Bias bias sorting coevolution Convergence Evolutionary algorithms Evolutionary computation Maintenance engineering Many-objective optimization problems (MaOPs) multi-objective evolutionary algorithm (MOEA) Multiple objective analysis Optimization Pareto optimization Populations Sociology Sorting Sorting algorithms Statistics
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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