An evolutionary algorithm with clustering-based selection strategies for multi-objective optimization

This paper proposes an evolutionary algorithm with clustering-based selection strategies to deal with multi-objective optimization problems. In the proposed algorithm, two clustering based selection strategies, named local indicator selection and local crowding selection, have been devised to approp...

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Bibliographic Details
Published in:Information sciences Vol. 624; pp. 217 - 234
Main Authors: Zhou, Shenghao, Mo, Xiaomei, Wang, Zidong, Li, Qi, Chen, Tianxiang, Zheng, Yujun, Sheng, Weiguo
Format: Journal Article
Language:English
Published: Elsevier Inc 01.05.2023
Subjects:
Clustering
I∊+ indicator
Multi-objective evolution algorithm
Selection strategy
I∊+ indicator
Multi-objective evolution algorithm
Clustering
Selection strategy
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:This paper proposes an evolutionary algorithm with clustering-based selection strategies to deal with multi-objective optimization problems. In the proposed algorithm, two clustering based selection strategies, named local indicator selection and local crowding selection, have been devised to appropriately search the space. The local indicator selection is developed to select diverse and well-converged individuals for mating while the local crowding selection strategy is designed to maintain a set of evenly distributed individuals on the Pareto front for next generation of evolution. The proposed method is further enhanced by a clustering based crowding degree strategy, which is introduced to extract a uniformly distributed and convergent solutions as the final output. The performance of proposed algorithm has been evaluated on 31 benchmark problems and compared with related methods. The results clearly show the merits of proposed strategies and the proposed method could significantly outperform related methods to be compared.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2022.12.076