A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization

Convergence and diversity are interdependently handled during the evolutionary process by most existing many-objective evolutionary algorithms (MaOEAs). In such a design, the degraded performance of one would deteriorate the other, and only solutions with both are able to improve the performance of...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 23; H. 5; S. 748 - 761
Hauptverfasser:	Sun, Yanan, Xue, Bing, Zhang, Mengjie, Yen, Gary G.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Convergence Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic algorithms Many-objective evolutionary optimization algorithm Multiple objective analysis nadir point Optimization Pareto optimization Pareto optimum Pareto-optimal subspace Performance degradation Performance enhancement Performance evaluation Sociology Statistics Sun two-stage method
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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