A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm
The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction mo...
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| Vydáno v: | Information sciences Ročník 612; s. 638 - 654 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Inc
01.10.2022
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. |
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| AbstractList | The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. |
| Author | Zhong, Zhaoman Dai, Hongwei Sun, Jing Gan, Xingjia Gong, Dunwei Tang, Xiaoke |
| Author_xml | – sequence: 1 givenname: Jing surname: Sun fullname: Sun, Jing email: sunj@jou.edu.cn organization: School of Science, Jiangsu Ocean University, Lianyungang 222005, Jiangsu, China – sequence: 2 givenname: Xingjia surname: Gan fullname: Gan, Xingjia email: xjgan@jou.edu.cn organization: School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, Jiangsu, China – sequence: 3 givenname: Dunwei surname: Gong fullname: Gong, Dunwei email: dwgong@vip.163.com organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China – sequence: 4 givenname: Xiaoke surname: Tang fullname: Tang, Xiaoke email: 1445402435@qq.com organization: School of Science, Jiangsu Ocean University, Lianyungang 222005, Jiangsu, China – sequence: 5 givenname: Hongwei surname: Dai fullname: Dai, Hongwei email: hwdai@jou.edu.cn organization: School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, Jiangsu, China – sequence: 6 givenname: Zhaoman surname: Zhong fullname: Zhong, Zhaoman email: zmzhong@jou.edu.cn organization: School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, Jiangsu, China |
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