A two-archive model based evolutionary algorithm for multimodal multi-objective optimization problems
Multimodal multi-objective optimization (MMO) can offer more elegant solutions and provide diverse decisions to decision-makers in real world optimization problems. Many multimodal evolutionary mechanisms have been proposed to explore and exploit two solution spaces (i.e. decision space and objectiv...
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| Veröffentlicht in: | Applied soft computing Jg. 119; S. 108606 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.04.2022
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | Multimodal multi-objective optimization (MMO) can offer more elegant solutions and provide diverse decisions to decision-makers in real world optimization problems. Many multimodal evolutionary mechanisms have been proposed to explore and exploit two solution spaces (i.e. decision space and objective space) in recent years. However, most existing methods only use single evolutionary operator to generate offsprings and ignore the advantage of using hybrid evolutionary algorithm. Moreover, it is still a great challenge to balance the effectiveness and efficiency simultaneously in the evolutionary process of MMO. In view of this, an efficient Two-Archive model based multimodal evolutionary algorithm is proposed in this paper. Two parallel offspring generation mechanisms based on competitive particle swarm optimizer and differential evolution are applied to expand two solution spaces with different evolutionary requirements. Moreover, niching local search scheme and reverse vector mutation strategy play roles in achieving better convergence and diversity. Finally, 22 MMO test problems are used to validate the superiority of the proposed method by comparing it with 5 state-of-the-art MMO algorithms. The proposed method is also expanded to solve 9 feature selection problems for validating the effectiveness of the proposed method on real world applications.
•Two offspring generation mechanisms based on competitive PSO and DE are proposed.•Two diversity maintenance strategies named niching local search scheme and reverse vector mutation strategy are designed.•This paper proposes a novel multimodal multi-objective optimization algorithm with a new Two-Archive model.•Experiments validate that the proposed method can solve MMOPs effectively and efficiently with a relatively smaller population size.•The proposed algorithm is expanded to deal with 9 real world feature selection problems. |
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| AbstractList | Multimodal multi-objective optimization (MMO) can offer more elegant solutions and provide diverse decisions to decision-makers in real world optimization problems. Many multimodal evolutionary mechanisms have been proposed to explore and exploit two solution spaces (i.e. decision space and objective space) in recent years. However, most existing methods only use single evolutionary operator to generate offsprings and ignore the advantage of using hybrid evolutionary algorithm. Moreover, it is still a great challenge to balance the effectiveness and efficiency simultaneously in the evolutionary process of MMO. In view of this, an efficient Two-Archive model based multimodal evolutionary algorithm is proposed in this paper. Two parallel offspring generation mechanisms based on competitive particle swarm optimizer and differential evolution are applied to expand two solution spaces with different evolutionary requirements. Moreover, niching local search scheme and reverse vector mutation strategy play roles in achieving better convergence and diversity. Finally, 22 MMO test problems are used to validate the superiority of the proposed method by comparing it with 5 state-of-the-art MMO algorithms. The proposed method is also expanded to solve 9 feature selection problems for validating the effectiveness of the proposed method on real world applications.
•Two offspring generation mechanisms based on competitive PSO and DE are proposed.•Two diversity maintenance strategies named niching local search scheme and reverse vector mutation strategy are designed.•This paper proposes a novel multimodal multi-objective optimization algorithm with a new Two-Archive model.•Experiments validate that the proposed method can solve MMOPs effectively and efficiently with a relatively smaller population size.•The proposed algorithm is expanded to deal with 9 real world feature selection problems. |
| ArticleNumber | 108606 |
| Author | Wang, Yanli Ashraf, Usman Yue, Caitong Hu, Yi Liang, Jing Yu, Kunjie Wang, Jie |
| Author_xml | – sequence: 1 givenname: Yi surname: Hu fullname: Hu, Yi organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China – sequence: 2 givenname: Jie surname: Wang fullname: Wang, Jie organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China – sequence: 3 givenname: Jing surname: Liang fullname: Liang, Jing email: liangjing@zzu.edu.cn organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China – sequence: 4 givenname: Yanli surname: Wang fullname: Wang, Yanli organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China – sequence: 5 givenname: Usman orcidid: 0000-0002-4423-0815 surname: Ashraf fullname: Ashraf, Usman organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China – sequence: 6 givenname: Caitong orcidid: 0000-0002-3362-0703 surname: Yue fullname: Yue, Caitong organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China – sequence: 7 givenname: Kunjie surname: Yu fullname: Yu, Kunjie organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China |
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| Keywords | Differential evolution Two-Archive Particle swarm optimizer Evolutionary algorithm Multimodal multi-objective optimization |
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