A many-objective evolutionary algorithm based on reference vector guided selection and two diversity and convergence enhancement strategies
Achieving the balance between convergence and diversity is a key and challenging issue in many-objective optimization. Reference vector guided selection is an exemplary method for decomposition-based many-objective evolutionary algorithms (MaOEAs). However, there are some problems with it such as in...
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| Published in: | Applied soft computing Vol. 154; p. 111369 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.03.2024
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | Achieving the balance between convergence and diversity is a key and challenging issue in many-objective optimization. Reference vector guided selection is an exemplary method for decomposition-based many-objective evolutionary algorithms (MaOEAs). However, there are some problems with it such as insufficient number of obtained solutions and inefficient convergence evaluation metric. Aiming at solving or alleviating these problems, this paper proposes a many-objective evolutionary algorithm based on reference vector guided selection and two diversity and convergence enhancement strategies. The proposed algorithm introduces two new strategies namely adaptive sparse region detection and convergence-only selection. The former is to adaptively detect sparse regions of current elite population, while the latter is to prevent the elimination of solutions with prominent convergence performance. Together with a newly proposed elite retention strategy, these two strategies can achieve diversity and convergence enhancement on the basis on reference vector guided selection. Besides, A new selection criterion for reference vector guided selection is proposed to better measure the convergence of solutions in high dimensionality. Experimental results on widely used test problem suites up to 15 objectives indicate that the proposed algorithm is highly competitive in comparison with seven state-of-the-art MaOEAs.
•Two strategies based on reference vector guided selection for better performance.•An adaptive approach to detect sparse regions for diversity enhancement.•A method to preserve prominent solutions for convergence enhancement.•An elite retention strategy to fill vacancies in elite population. |
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| AbstractList | Achieving the balance between convergence and diversity is a key and challenging issue in many-objective optimization. Reference vector guided selection is an exemplary method for decomposition-based many-objective evolutionary algorithms (MaOEAs). However, there are some problems with it such as insufficient number of obtained solutions and inefficient convergence evaluation metric. Aiming at solving or alleviating these problems, this paper proposes a many-objective evolutionary algorithm based on reference vector guided selection and two diversity and convergence enhancement strategies. The proposed algorithm introduces two new strategies namely adaptive sparse region detection and convergence-only selection. The former is to adaptively detect sparse regions of current elite population, while the latter is to prevent the elimination of solutions with prominent convergence performance. Together with a newly proposed elite retention strategy, these two strategies can achieve diversity and convergence enhancement on the basis on reference vector guided selection. Besides, A new selection criterion for reference vector guided selection is proposed to better measure the convergence of solutions in high dimensionality. Experimental results on widely used test problem suites up to 15 objectives indicate that the proposed algorithm is highly competitive in comparison with seven state-of-the-art MaOEAs.
•Two strategies based on reference vector guided selection for better performance.•An adaptive approach to detect sparse regions for diversity enhancement.•A method to preserve prominent solutions for convergence enhancement.•An elite retention strategy to fill vacancies in elite population. |
| ArticleNumber | 111369 |
| Author | Wang, Dongya Yang, Lei Cao, Jiale Li, Kangshun Zhang, Yuanye |
| Author_xml | – sequence: 1 givenname: Lei orcidid: 0000-0003-3143-6563 surname: Yang fullname: Yang, Lei email: yanglei_s@scau.edu.cn organization: School of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China – sequence: 2 givenname: Yuanye surname: Zhang fullname: Zhang, Yuanye organization: School of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China – sequence: 3 givenname: Jiale surname: Cao fullname: Cao, Jiale organization: School of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China – sequence: 4 givenname: Kangshun surname: Li fullname: Li, Kangshun organization: School of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China – sequence: 5 givenname: Dongya surname: Wang fullname: Wang, Dongya organization: University of Exeter, College of Engineering, Mathematics and Physical Sciences, Exeter EX4 4QF, UK |
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| Keywords | Reference vector Convergence and diversity enhancement Many-objective optimization |
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