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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Bibliographic Details

Published in:	Engineering optimization Vol. 57; no. 11; pp. 3344 - 3377
Main Authors:	Ren, Xuepeng, Wang, Maocai, Dai, Guangming, Peng, Lei, Chen, Xiaoyu, Song, Zhiming
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 02.11.2025 Taylor & Francis Ltd
Subjects:	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
Online Access:	Get full text
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