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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| Vydáno v: | Information sciences Ročník 679; s. 121077 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Inc
01.09.2024
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| ISSN: | 0020-0255, 1872-6291 |
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| 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.
•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. |
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| AbstractList | 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. |
| ArticleNumber | 121077 |
| Author | Zhang, Chen Ye, Chuanlong Duan, Yansong He, Fazhi Gao, Xiaoxin Bai, Junwei |
| Author_xml | – sequence: 1 givenname: Xiaoxin surname: Gao fullname: Gao, Xiaoxin email: gao.xiaoxin.cn@whu.edu.cn organization: School of Computer Science, Wuhan University, Wuhan 430072, China – sequence: 2 givenname: Fazhi orcidid: 0000-0001-7016-3698 surname: He fullname: He, Fazhi email: fzhe@whu.edu.cn organization: School of Computer Science, Wuhan University, Wuhan 430072, China – sequence: 3 givenname: Yansong orcidid: 0000-0002-8037-7638 surname: Duan fullname: Duan, Yansong email: ysduan@whu.edu.cn organization: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China – sequence: 4 givenname: Chuanlong surname: Ye fullname: Ye, Chuanlong email: chuanlongye@whu.edu.cn organization: School of Computer Science, Wuhan University, Wuhan 430072, China – sequence: 5 givenname: Junwei surname: Bai fullname: Bai, Junwei email: whu_cs_baijw@whu.edu.cn organization: School of Computer Science, Wuhan University, Wuhan 430072, China – sequence: 6 givenname: Chen orcidid: 0000-0003-1919-0652 surname: Zhang fullname: Zhang, Chen email: zhangchen@upc.edu.cn organization: College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, Shandong, China |
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| Keywords | Environmental selection Convergence and diversity Large-scale many-objective evolutionary algorithms Space sampling Reference vector |
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