A clustering-based competitive particle swarm optimization with grid ranking for multi-objective optimization problems-Reference-Cited by-同舟云学术

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

Published:2023-07-20 Issue:1 Volume:13 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Ye Qianlin,Wang Zheng,Zhao Yanwei,Dai Rui,Wu Fei,Yu Mengjiao

Abstract

AbstractThe 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.

Funder

the National Natural Science Foundation of China

the Open Project Program of the State Key Lab of CAD&CG

the Foundation of State Key Laboratory of Digital Manufacturing Equipment and Technology

the Key Research and Development Program of Zhejiang Province

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

https://www.nature.com/articles/s41598-023-38529-4.pdf

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