From penalty function to learning-driven: evolution and perspectives of constrained multi-objective evolutionary algorithm

With the increasing prevalence of constrained multi-objective optimization problems (CMOPs) in complex systems such as engineering design and intelligent manufacturing, reconciling constraints and multi-objective conflicts in dynamic environments has become a key challenge in evolutionary algorithm...

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Veröffentlicht in:	Swarm and evolutionary computation Jg. 96; S. 102006
Hauptverfasser:	Hu, Min, Huang, Gang, Yang, Xueying, Xiao, Jinwei, Wang, Xun, Zhang, Tiantian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.07.2025
Schlagworte:	Constrained multi-objective evolutionary algorithm Constrained multi-objective optimization problem Learning-driven Penalty function Penalty function Constrained multi-objective optimization problem Constrained multi-objective evolutionary algorithm Learning-driven
ISSN:	2210-6502
Online-Zugang:	Volltext
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