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段延松作为共同通讯在 《Information Sciences》上发表文章“A space sampling based large-scale many-objective evolutionary algorithm”

浏览: 时间:2024-07-20 19:39 发布人:段延松

段延松作为共同通讯在 《Information Sciences》上发表文章“A space sampling based large-scale many-objective evolutionary algorithm”


Cite:


Xiaoxin Gao, Fazhi He, Yansong Duan, Chuanlong Ye, Junwei Bai, Chen Zhang,

A space sampling based large-scale many-objective evolutionary algorithm,

Information Sciences,Volume 679,2024,121077,ISSN 0020-0255,

https://doi.org/10.1016/j.ins.2024.121077.



Elsevier

Information Sciences

Volume 679, September 2024, 121077
Information Sciences

A space sampling based large-scale many-objective evolutionary algorithm

https://doi.org/10.1016/j.ins.2024.121077Get rights and content

Highlights

  • A space sampling based algorithm for large-scale many-objective optimization problems is developed.

    An individual-linkage sampling strategy for sampling in the large-scale decision space is introduced.

  • An environmental selection strategy based on nondominated sorting and reference vector association is introduced.


Abstract

Large-scale multiobjective optimization problems have attracted increasing attention in both engineering applications and scientific research. Academically, large-scale multiobjective problems involve hundreds or thousands of decision variables. Due to the large decision space, the performance of traditional multiobjective evolutionary algorithms decreases dramatically when dealing with large-scale multiobjective problems, especially many-objective problems. With this in mind, a space sampling based large-scale many-objective evolutionary algorithm (LSMaOEA) is proposed in this article. Specifically, a space sampling method is developed that alternately performs upper/lower-linkage sampling and individual-linkage sampling to sample a set of individuals in the decision space. An environmental selection strategy based on nondominated sorting and reference vector association is proposed. Thus, the proposed LSMaOEA can alleviate excessively dense sampling at boundaries and improve the diversity of existing space sampling based algorithms for large-scale many-objective problems. In the experiments, the proposed algorithm is assessed by comparing it with eight state-of-the-art multi/many-objective evolutionary algorithms. The evaluation is conducted using two popular indicators across nine challenging multiobjective optimization benchmark problems with up to 2000 decision variables. The extensive experimental results consistently reveal that the proposed algorithm outperforms all the compared algorithms.

Keywords

Large-scale many-objective evolutionary algorithms
Space sampling
Environmental selection
Reference vector
Convergence and diversity