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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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
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Abstract	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 towards the centre. This article introduces a more accurate method for assessing individual sparsity in the objective space using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The contributions of this work include (1) identifying the limitations of using Euclidean distance for sparsity measurement, (2) mitigating DBSCAN sensitivity to input parameters through adaptive adjustment via a genetic algorithm and (3) integrating DBSCAN with multi-objective algorithms to address the limitations of Euclidean distance by leveraging core and boundary points. Experimental results on three benchmark test problems and an engineering application involving mechanical bearings highlight the proposed algorithm's strong performance and competitiveness.
AbstractList	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 towards the centre. This article introduces a more accurate method for assessing individual sparsity in the objective space using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The contributions of this work include (1) identifying the limitations of using Euclidean distance for sparsity measurement, (2) mitigating DBSCAN sensitivity to input parameters through adaptive adjustment via a genetic algorithm and (3) integrating DBSCAN with multi-objective algorithms to address the limitations of Euclidean distance by leveraging core and boundary points. Experimental results on three benchmark test problems and an engineering application involving mechanical bearings highlight the proposed algorithm's strong performance and competitiveness.
Author	Ren, Xuepeng Song, Zhiming Peng, Lei Dai, Guangming Wang, Maocai Chen, Xiaoyu
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SubjectTerms	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
Title	Revolutionizing population sparsity assessment: machine learning-powered solutions for multi-objective evolutionary algorithms
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