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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Vydané v:	Applied intelligence (Dordrecht, Netherlands) Ročník 53; číslo 9; s. 10380 - 10416
Hlavní autori:	Cao, Jie, Yan, Zesen, Chen, Zuohan, Zhang, Jianlin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.05.2023 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
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