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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Vydané v:	IEEE transactions on evolutionary computation Ročník 23; číslo 5; s. 748 - 761
Hlavní autori:	Sun, Yanan, Xue, Bing, Zhang, Mengjie, Yen, Gary G.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.10.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
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