A clustering-based competitive particle swarm optimization with grid ranking for multi-objective optimization problems

The goal of the multi-objective optimization algorithm is to quickly and accurately find a set of trade-off solutions. This paper develops a clustering-based competitive multi-objective particle swarm optimizer using the enhanced grid for solving multi-objective optimization problems, named EGC-CMOP...

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Vydáno v:Scientific reports Ročník 13; číslo 1; s. 11754 - 13
Hlavní autoři: Ye, Qianlin, Wang, Zheng, Zhao, Yanwei, Dai, Rui, Wu, Fei, Yu, Mengjiao
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 20.07.2023
Nature Publishing Group
Nature Portfolio
Témata:
639/166
639/166/987
Algorithms
Humanities and Social Sciences
multidisciplinary
Optimization
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
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Shrnutí:The goal of the multi-objective optimization algorithm is to quickly and accurately find a set of trade-off solutions. This paper develops a clustering-based competitive multi-objective particle swarm optimizer using the enhanced grid for solving multi-objective optimization problems, named EGC-CMOPSO. The enhanced grid mechanism involved in EGC-CMOPSO is designed to locate superior Pareto optimal solutions. Subsequently, a hierarchical-based clustering is established on the grid for improving the accuracy rate of the grid selection. Due to the adaptive division of clustering centers, EGC-CMOPSO is applicable for solving MOPs with various Pareto front (PF) shapes. Particularly, the inferior solutions are discarded and the leading particles are identified by the comprehensive ranking of particles in each cluster. Finally, the selected leading particles compete against each other, and the winner guides the update of the current particle. The proposed EGC-CMOPSO and the eight latest multi-objective optimization algorithms are performed on 21 test problems. The experimental results validate that the proposed EGC-CMOPSO is capable of handling multi-objective optimization problems (MOPs) and obtaining superior performance on both convergence and diversity.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-38529-4