A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

Dynamic interval multiobjective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms (EAs) that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multiobjective cooperative co-evolutionary optimization...

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Published in:	IEEE transactions on evolutionary computation Vol. 24; no. 1; pp. 142 - 156
Main Authors:	Gong, Dunwei, Xu, Biao, Zhang, Yong, Guo, Yinan, Yang, Shengxiang
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Cooperative co-evolutionary optimization dynamic optimization Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic algorithms interval similarity Mopping multiobjective optimization Multiple objective analysis Optimization Pareto optimization Probability distribution Programming response strategy Robots Similarity Sociology Strategy Variables
ISSN:	1089-778X, 1941-0026
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Abstract	Dynamic interval multiobjective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms (EAs) that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multiobjective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two subpopulations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances as well as a multiperiod portfolio selection problem and compared with five state-of-the-art EAs. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances.
AbstractList	Dynamic interval multiobjective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms (EAs) that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multiobjective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two subpopulations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances as well as a multiperiod portfolio selection problem and compared with five state-of-the-art EAs. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances.
Author	Yang, Shengxiang Guo, Yinan Gong, Dunwei Xu, Biao Zhang, Yong
Author_xml	– sequence: 1 givenname: Dunwei orcidid: 0000-0002-0363-4207 surname: Gong fullname: Gong, Dunwei email: dwgong@vip.163.com organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China – sequence: 2 givenname: Biao orcidid: 0000-0002-9551-8579 surname: Xu fullname: Xu, Biao email: xubiao512@163.com organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China – sequence: 3 givenname: Yong surname: Zhang fullname: Zhang, Yong email: yongzh401@126.com organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China – sequence: 4 givenname: Yinan orcidid: 0000-0002-4276-5410 surname: Guo fullname: Guo, Yinan email: guoyinan@cumt.edu.cn organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China – sequence: 5 givenname: Shengxiang orcidid: 0000-0001-7222-4917 surname: Yang fullname: Yang, Shengxiang email: syang@dmu.ac.uk organization: Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, Leicester, U.K
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Snippet	Dynamic interval multiobjective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms...
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SubjectTerms	Cooperative co-evolutionary optimization dynamic optimization Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic algorithms interval similarity Mopping multiobjective optimization Multiple objective analysis Optimization Pareto optimization Probability distribution Programming response strategy Robots Similarity Sociology Strategy Variables
Title	A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems
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