An Indicator-Based Evolutionary Algorithm for Large-Scale Constrained Multi-objective Optimization

Most existing constrained multi-objective evolutionary algorithms (CMOEAs) experience a dramatic performance degradation when solving large-scale constrained multi-objective optimization problems (LSCMOPs), since they converge very slowly and easily get trapped in local optima due to the loss of div...

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Vydané v:	IEEE transactions on evolutionary computation s. 1
Hlavní autori:	Zhong, Xiaoyu, Yao, Xiangjuan, Qiao, Kangjia, Gong, Dunwei, Jin, Yaochu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 2025
Predmet:	Benchmark testing Constrained multi-objective optimization Convergence differential evolution Diversity reception Electronic mail Evolutionary computation indicator large-scale Optimization Scalability Search problems Training variable grouping Vectors
ISSN:	1089-778X, 1941-0026
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