A Pareto front estimation-based constrained multi-objective evolutionary algorithm

The balance of convergence, diversity, and feasibility plays a pivotal role in constrained multi-objective optimization problems. To address this issue, in this paper a novel method named PeCMOEA is proposed, in which the pivotal solutions, which are designed for estimating the constrained Pareto fr...

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Veröffentlicht in:	Applied intelligence (Dordrecht, Netherlands) Jg. 53; H. 9; S. 10380 - 10416
Hauptverfasser:	Cao, Jie, Yan, Zesen, Chen, Zuohan, Zhang, Jianlin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.05.2023 Springer Nature B.V
Schlagworte:	Artificial Intelligence Computer Science Convergence Estimation Evolutionary algorithms Feasibility Fitness Genetic algorithms Machines Manufacturing Mechanical Engineering Methods Multiple objective analysis Optimization Pareto optimization Penalty function Populations Processes Constrained multi-objective optimization Multiple-populations Constrained multi-objective evolutionary algorithms Pareto front curvature
ISSN:	0924-669X, 1573-7497
Online-Zugang:	Volltext
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