Many-Objective Evolutionary Algorithm based on Dominance Degree
For many-objective optimization problems, the comparability of non-dominated solutions is always an essential and fundamental issue. Due to the inefficiency of Pareto dominance for many-objective optimization problems, various improved dominance relations have been proposed to enhance the evolutiona...
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| Published in: | Applied soft computing Vol. 113; p. 107869 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.12.2021
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | For many-objective optimization problems, the comparability of non-dominated solutions is always an essential and fundamental issue. Due to the inefficiency of Pareto dominance for many-objective optimization problems, various improved dominance relations have been proposed to enhance the evolutionary pressure. However, these variants have one thing in common that they treat each solution in a static manner, and the relations between any two solutions are just defined as a kind of static spatial adjacencies, resulting in the unquantifiable comparability. Different from them, this paper proposes a dominance degree metric, which treats solutions as different stages of a dynamic motion process. The dynamic motion process represents the continuous changes of the degree of one solution from Pareto dominating others to being Pareto dominated by others. Based on the dominance degree, this paper proposes a Many-Objective Evolutionary Algorithm based on Dominance Degree, in which the mating selection and environmental update strategies are redesigned accordingly. The proposed method is comprehensively tested with several state-of-the-art optimizers on two popular test suites and practical multi-point distance minimization problems. Experimental results demonstrate its effectiveness and superiority over other optimizers in terms of the convergence, diversity and spread.
•It is observed that regular methods for dominance relations are described in a static manner.•Dominance degree is proposed for reflecting the dynamic relationships of solutions.•Many-objective Evolutionary Algorithm based on Dominance Degree is proposed and tested. |
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| AbstractList | For many-objective optimization problems, the comparability of non-dominated solutions is always an essential and fundamental issue. Due to the inefficiency of Pareto dominance for many-objective optimization problems, various improved dominance relations have been proposed to enhance the evolutionary pressure. However, these variants have one thing in common that they treat each solution in a static manner, and the relations between any two solutions are just defined as a kind of static spatial adjacencies, resulting in the unquantifiable comparability. Different from them, this paper proposes a dominance degree metric, which treats solutions as different stages of a dynamic motion process. The dynamic motion process represents the continuous changes of the degree of one solution from Pareto dominating others to being Pareto dominated by others. Based on the dominance degree, this paper proposes a Many-Objective Evolutionary Algorithm based on Dominance Degree, in which the mating selection and environmental update strategies are redesigned accordingly. The proposed method is comprehensively tested with several state-of-the-art optimizers on two popular test suites and practical multi-point distance minimization problems. Experimental results demonstrate its effectiveness and superiority over other optimizers in terms of the convergence, diversity and spread.
•It is observed that regular methods for dominance relations are described in a static manner.•Dominance degree is proposed for reflecting the dynamic relationships of solutions.•Many-objective Evolutionary Algorithm based on Dominance Degree is proposed and tested. |
| ArticleNumber | 107869 |
| Author | Li, Wuzhao Wu, Qidi Min, Jun Zhang, Maoqing Wang, Lei Guo, Weian Pang, Junwei Liu, Hanwei |
| Author_xml | – sequence: 1 givenname: Maoqing surname: Zhang fullname: Zhang, Maoqing email: maoqing_zhang@163.com organization: School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China – sequence: 2 givenname: Lei orcidid: 0000-0002-2146-9081 surname: Wang fullname: Wang, Lei email: wanglei@tongji.edu.cn organization: School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China – sequence: 3 givenname: Weian surname: Guo fullname: Guo, Weian email: guoweian@163.com organization: Sino-Germany College of Applied Sciences, Tongji University, Shanghai, 201804, China – sequence: 4 givenname: Wuzhao orcidid: 0000-0001-8839-913X surname: Li fullname: Li, Wuzhao email: rick_2006003@139.com organization: Wenzhou Polytechnic, 325000, Zhejiang, China – sequence: 5 givenname: Junwei surname: Pang fullname: Pang, Junwei email: 1932986@tongji.edu.cn organization: School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China – sequence: 6 givenname: Jun surname: Min fullname: Min, Jun email: 15205182192@163.com organization: School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China – sequence: 7 givenname: Hanwei surname: Liu fullname: Liu, Hanwei email: liuhw1@tongji.edu.cn organization: School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China – sequence: 8 givenname: Qidi surname: Wu fullname: Wu, Qidi organization: School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China |
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| Keywords | Dominance relations Dominance degree Evolutionary algorithm Pareto dominance |
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