A Dual-Population-Based NSGA-III for Constrained Many-Objective Optimization

The main challenge for constrained many-objective optimization problems (CMaOPs) is how to achieve a balance between feasible and infeasible solutions. Most of the existing constrained many-objective evolutionary algorithms (CMaOEAs) are feasibility-driven, neglecting the maintenance of population c...

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Vydané v:	Entropy (Basel, Switzerland) Ročník 25; číslo 1; s. 13
Hlavní autori:	Geng, Huantong, Zhou, Zhengli, Shen, Junye, Song, Feifei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 21.12.2022 MDPI
Predmet:	Archives & records coevolution Collaboration constrained many-objective optimization Constraints Design dual-population evolutionary algorithm Evolutionary algorithms Feasibility Genetic algorithms Methods Multiple objective analysis Optimization Populations ε-constraint handling constrained many-objective optimization ε-constraint handling coevolution dual-population evolutionary algorithm
ISSN:	1099-4300, 1099-4300
On-line prístup:	Získať plný text
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