Revolutionizing population sparsity assessment: machine learning-powered solutions for multi-objective evolutionary algorithms

Decomposed multi-objective evolutionary algorithms have recently gained attention in research, with population sparsity often evaluated through Euclidean distance. However, individuals with high sparsity tend to be located along the edges of the Pareto front, whereas those with low sparsity cluster...

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Vydáno v:	Engineering optimization Ročník 57; číslo 11; s. 3344 - 3377
Hlavní autoři:	Ren, Xuepeng, Wang, Maocai, Dai, Guangming, Peng, Lei, Chen, Xiaoyu, Song, Zhiming
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Abingdon Taylor & Francis 02.11.2025 Taylor & Francis Ltd
Témata:	adaptive weight vector adjustment Clustering Euclidean geometry evolutionary algorithm Evolutionary algorithms Genetic algorithms Machine learning Multi-objective optimization Multiple objective analysis Parameter sensitivity population sparsity
ISSN:	0305-215X, 1029-0273
On-line přístup:	Získat plný text
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