Dynamic multi-objective evolutionary algorithm based on knowledge transfer
Dynamic multi-objective optimization problems (DMOPs) are mainly reflected in objective changes with changes in the environment. To solve DMOPs, a transfer learning (TL) approach is used, which can continuously adapt to environmental changes and reuse valuable knowledge from the past. However, if al...
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| Vydané v: | Information sciences Ročník 636; s. 118886 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Inc
01.07.2023
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | Dynamic multi-objective optimization problems (DMOPs) are mainly reflected in objective changes with changes in the environment. To solve DMOPs, a transfer learning (TL) approach is used, which can continuously adapt to environmental changes and reuse valuable knowledge from the past. However, if all individuals are transferred, they may experience negative transfers. Therefore, this paper proposes a novel knowledge transfer method for the dynamic multi-objective evolutionary algorithm (T-DMOEA) to solve DMOPs, which consists of a multi-time prediction model (MTPM) and a manifold TL algorithm. First, according to the movement trend of historical knee points, the MTPM model uses a weighted method to effectively track knee points after environmental changes. Then, the knowledge of the suboptimal solution is reused in the non-knee point set using the manifold TL technique, which yields more high-quality individuals and speeds up the convergence. In the dynamic evolutionary process, the knee points and high-quality solutions are combined to guide the generation of the initial population in the next environment, ensuring the diversity of the population while reducing the computational cost. The experimental results show that the proposed T-DMOEA algorithm can converge rapidly in solving DMOPs while obtaining better-quality solutions. |
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| AbstractList | Dynamic multi-objective optimization problems (DMOPs) are mainly reflected in objective changes with changes in the environment. To solve DMOPs, a transfer learning (TL) approach is used, which can continuously adapt to environmental changes and reuse valuable knowledge from the past. However, if all individuals are transferred, they may experience negative transfers. Therefore, this paper proposes a novel knowledge transfer method for the dynamic multi-objective evolutionary algorithm (T-DMOEA) to solve DMOPs, which consists of a multi-time prediction model (MTPM) and a manifold TL algorithm. First, according to the movement trend of historical knee points, the MTPM model uses a weighted method to effectively track knee points after environmental changes. Then, the knowledge of the suboptimal solution is reused in the non-knee point set using the manifold TL technique, which yields more high-quality individuals and speeds up the convergence. In the dynamic evolutionary process, the knee points and high-quality solutions are combined to guide the generation of the initial population in the next environment, ensuring the diversity of the population while reducing the computational cost. The experimental results show that the proposed T-DMOEA algorithm can converge rapidly in solving DMOPs while obtaining better-quality solutions. |
| ArticleNumber | 118886 |
| Author | Zhao, Tianhao Xie, Liping Cai, Xingjuan Wu, Linjie Wu, Di |
| Author_xml | – sequence: 1 givenname: Linjie surname: Wu fullname: Wu, Linjie organization: School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024, China – sequence: 2 givenname: Di surname: Wu fullname: Wu, Di organization: Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China – sequence: 3 givenname: Tianhao surname: Zhao fullname: Zhao, Tianhao organization: School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024, China – sequence: 4 givenname: Xingjuan orcidid: 0000-0003-1300-779X surname: Cai fullname: Cai, Xingjuan email: xingjuancai@163.com organization: School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024, China – sequence: 5 givenname: Liping surname: Xie fullname: Xie, Liping email: xieliping@tyust.edu.cn organization: School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024, China |
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| Keywords | Dynamic multi-objective optimization Knowledge transfer Predictive model Manifold transfer learning |
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