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

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 tr...

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Veröffentlicht in:Information sciences Jg. 679; S. 121077
Hauptverfasser: Gao, Xiaoxin, He, Fazhi, Duan, Yansong, Ye, Chuanlong, Bai, Junwei, Zhang, Chen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.09.2024
Schlagworte:
Convergence and diversity
Environmental selection
Large-scale many-objective evolutionary algorithms
Reference vector
Space sampling
Environmental selection
Convergence and diversity
Large-scale many-objective evolutionary algorithms
Space sampling
Reference vector
ISSN:0020-0255, 1872-6291
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Zusammenfassung: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. •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.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2024.121077