Fusion prediction strategy-based dynamic multi-objective sparrow search algorithm
Solving dynamic multi-objective optimization problems with time-varying Pareto front (PF) or Pareto set (PS) is a challenging task. Such problems require algorithms to react to environmental changes and efficiently track optimal solutions. For this purpose, a dynamic multi-objective sparrow search a...
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| Vydáno v: | Applied soft computing Ročník 165; s. 112071 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.11.2024
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| Témata: | |
| ISSN: | 1568-4946 |
| On-line přístup: | Získat plný text |
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| Abstract | Solving dynamic multi-objective optimization problems with time-varying Pareto front (PF) or Pareto set (PS) is a challenging task. Such problems require algorithms to react to environmental changes and efficiently track optimal solutions. For this purpose, a dynamic multi-objective sparrow search algorithm (SSA) with fusion prediction strategy, based on difference model and kernel extreme learning machine (DMOSSA-FPS), is proposed. Given the diversity of change characteristics, a single prediction model is insufficient. Therefore, based on the historical information of the population, a difference model and a kernel extreme learning machine are integrated for PS prediction. The former is used to predict the solutions of some individuals under approximate linear changes and the latter is employed for nonlinear predictions. In a new environment, the combined predictions increase the diversity of the initial population. Additionally, a new static optimizer is proposed, which combines decomposition- and dominance-based approaches to constitute a new individual screening mechanism. Then the optimization mode of SSA is introduced to enhance both algorithmic diversity and convergence rate. The experimental results on the DF test suite demonstrate that, compared with several other advanced algorithms, DMOSSA-FPS exhibits stronger convergence and robustness.
•A novel method to solve dynamic multi-objective problems is proposed (DMOSSA-FPS).•Designing a fusion prediction strategy to complete the dynamic response.•Dominance degree sorting (DDS) is introduced to speed up the sorting.•A new static optimizer based on dominance and decomposition is proposed (MOSSA/DD). |
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| AbstractList | Solving dynamic multi-objective optimization problems with time-varying Pareto front (PF) or Pareto set (PS) is a challenging task. Such problems require algorithms to react to environmental changes and efficiently track optimal solutions. For this purpose, a dynamic multi-objective sparrow search algorithm (SSA) with fusion prediction strategy, based on difference model and kernel extreme learning machine (DMOSSA-FPS), is proposed. Given the diversity of change characteristics, a single prediction model is insufficient. Therefore, based on the historical information of the population, a difference model and a kernel extreme learning machine are integrated for PS prediction. The former is used to predict the solutions of some individuals under approximate linear changes and the latter is employed for nonlinear predictions. In a new environment, the combined predictions increase the diversity of the initial population. Additionally, a new static optimizer is proposed, which combines decomposition- and dominance-based approaches to constitute a new individual screening mechanism. Then the optimization mode of SSA is introduced to enhance both algorithmic diversity and convergence rate. The experimental results on the DF test suite demonstrate that, compared with several other advanced algorithms, DMOSSA-FPS exhibits stronger convergence and robustness.
•A novel method to solve dynamic multi-objective problems is proposed (DMOSSA-FPS).•Designing a fusion prediction strategy to complete the dynamic response.•Dominance degree sorting (DDS) is introduced to speed up the sorting.•A new static optimizer based on dominance and decomposition is proposed (MOSSA/DD). |
| ArticleNumber | 112071 |
| Author | Huang, Hefan Wei, Jianan Wu, Rui Huang, Haisong Wang, Shixin Zhu, Yunwei Gu, Qiang Han, Zhenggong |
| Author_xml | – sequence: 1 givenname: Rui surname: Wu fullname: Wu, Rui email: wr_1999@163.com organization: Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China – sequence: 2 givenname: Haisong orcidid: 0000-0002-3750-6537 surname: Huang fullname: Huang, Haisong email: hshuang@gzu.edu.cn organization: Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China – sequence: 3 givenname: Jianan surname: Wei fullname: Wei, Jianan email: jawei@gzu.edu.cn organization: Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China – sequence: 4 givenname: Hefan surname: Huang fullname: Huang, Hefan email: hefan_huang@163.com organization: Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China – sequence: 5 givenname: Shixin surname: Wang fullname: Wang, Shixin email: Y30220700@mail.ecust.edu.cn organization: School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China – sequence: 6 givenname: Yunwei surname: Zhu fullname: Zhu, Yunwei email: gs.ywzhu19@gzu.edu.cn organization: Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China – sequence: 7 givenname: Zhenggong surname: Han fullname: Han, Zhenggong email: zghan0121@126.com organization: Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China – sequence: 8 givenname: Qiang surname: Gu fullname: Gu, Qiang email: gs.qku21@gzu.edu.cn organization: Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China |
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| Keywords | Fusion prediction strategy Dominance degree sorting Kernel Extreme Learning Machine Dynamic multi-objective optimization Sparrow Search Algorithm |
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| Title | Fusion prediction strategy-based dynamic multi-objective sparrow search algorithm |
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