An evolutionary algorithm with clustering-based selection strategies for multi-objective optimization
Publication Date
5-1-2023
Document Type
Article
Publication Title
Information Sciences
Volume
624
DOI
10.1016/j.ins.2022.12.076
First Page
217
Last Page
234
Abstract
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.
Funding Number
61872123
Funding Sponsor
Royal Society
Keywords
Clustering, I∊+ indicator, Multi-objective evolution algorithm, Selection strategy
Department
Computer Science
Recommended Citation
Shenghao Zhou, Xiaomei Mo, Zidong Wang, Qi Li, Tianxiang Chen, Yujun Zheng, and Weiguo Sheng. "An evolutionary algorithm with clustering-based selection strategies for multi-objective optimization" Information Sciences (2023): 217-234. https://doi.org/10.1016/j.ins.2022.12.076
