Push and pull search embedded in an M2M framework for solving constrained multi-objective optimization problems

In dealing with constrained multi-objective optimization problems (CMOPs), a key issue of multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity of working populations. However, most state-of-the-art MOEAs show poor performance in balancing them, and can cause th...

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Veröffentlicht in:	Swarm and evolutionary computation Jg. 54; S. 100651
Hauptverfasser:	Fan, Zhun, Wang, Zhaojun, Li, Wenji, Yuan, Yutong, You, Yugen, Yang, Zhi, Sun, Fuzan, Ruan, Jie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.05.2020
Schlagworte:	Constrained multi-objective evolutionary algorithms Constraint-handling mechanisms Multi-objective to multi-objective (M2M) decomposition NSGA-II Push and pull search NSGA-II Constraint-handling mechanisms Constrained multi-objective evolutionary algorithms Push and pull search Multi-objective to multi-objective (M2M) decomposition
ISSN:	2210-6502
Online-Zugang:	Volltext
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