Multi‐Swarm Dynamic Crow Search Algorithm for Dynamic Multi‐Objective Optimization
Dynamic multi‐objective optimization problems (DMOPs) are one of the most challenging problems in real‐world systems. This paper proposes a multi‐swarm dynamic crow search algorithm (CSA) to solve DMOPs effectively and advance the application of CSA for DMOPs. Three components are introduced in the...
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| Vydáno v: | IEEJ transactions on electrical and electronic engineering |
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25.09.2025
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| ISSN: | 1931-4973, 1931-4981 |
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| Abstract | Dynamic multi‐objective optimization problems (DMOPs) are one of the most challenging problems in real‐world systems. This paper proposes a multi‐swarm dynamic crow search algorithm (CSA) to solve DMOPs effectively and advance the application of CSA for DMOPs. Three components are introduced in the algorithm. The multi‐swarm co‐evolution mechanism creates a distinct swarm for each optimization objective, while a memory time‐based archive update strategy is introduced. A complex behavior strategy is developed to adaptively adjust the key parameters and guide the swarms for fast convergence. The dynamism handling mechanism uses random re‐evaluation for change detection, proposes a split selection method, and a memory reuse strategy to choose old solutions with good diversity, and considers random re‐initialization and prediction‐based approaches to respond to the change. Extensive experiments demonstrate that the proposed algorithm is competitive in both optimization performance and computational cost when compared with state‐of‐the‐art methods. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. |
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| AbstractList | Dynamic multi‐objective optimization problems (DMOPs) are one of the most challenging problems in real‐world systems. This paper proposes a multi‐swarm dynamic crow search algorithm (CSA) to solve DMOPs effectively and advance the application of CSA for DMOPs. Three components are introduced in the algorithm. The multi‐swarm co‐evolution mechanism creates a distinct swarm for each optimization objective, while a memory time‐based archive update strategy is introduced. A complex behavior strategy is developed to adaptively adjust the key parameters and guide the swarms for fast convergence. The dynamism handling mechanism uses random re‐evaluation for change detection, proposes a split selection method, and a memory reuse strategy to choose old solutions with good diversity, and considers random re‐initialization and prediction‐based approaches to respond to the change. Extensive experiments demonstrate that the proposed algorithm is competitive in both optimization performance and computational cost when compared with state‐of‐the‐art methods. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. |
| Author | Diao, Xing‐Chun Liu, Kun Li, Qing Li, Geng‐Song Liu, Yi Zheng, Qi‐Bin |
| Author_xml | – sequence: 1 givenname: Geng‐Song surname: Li fullname: Li, Geng‐Song organization: National Innovation Institute of Defense Technology Beijing 100071 China – sequence: 2 givenname: Yi surname: Liu fullname: Liu, Yi organization: Academy of Military Sciences Beijing 100091 China – sequence: 3 givenname: Qing surname: Li fullname: Li, Qing organization: Shanghai Jiao Tong University Shanghai 200240 China – sequence: 4 givenname: Qi‐Bin surname: Zheng fullname: Zheng, Qi‐Bin organization: Academy of Military Sciences Beijing 100091 China – sequence: 5 givenname: Kun surname: Liu fullname: Liu, Kun organization: Academy of Military Sciences Beijing 100091 China – sequence: 6 givenname: Xing‐Chun surname: Diao fullname: Diao, Xing‐Chun organization: National Innovation Institute of Defense Technology Beijing 100071 China |
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