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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Veröffentlicht in:Engineering optimization Jg. 57; H. 11; S. 3344 - 3377
Hauptverfasser: Ren, Xuepeng, Wang, Maocai, Dai, Guangming, Peng, Lei, Chen, Xiaoyu, Song, Zhiming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 02.11.2025
Taylor & Francis Ltd
Schlagworte:
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-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2024.2434726