MICFOA: A Novel Improved Catch Fish Optimization Algorithm with Multi-Strategy for Solving Global Problems
Catch fish optimization algorithm (CFOA) is a newly proposed meta-heuristic algorithm based on human behaviors. CFOA shows better performance on multiple test functions and clustering problems. However, CFOA shows poor performance in some cases, and there is still room for improvement in convergence...
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MDPI AG
2024-08-01
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| author | Zhihao Fu Zhichun Li Yongkang Li Haoyu Chen |
| author_facet | Zhihao Fu Zhichun Li Yongkang Li Haoyu Chen |
| author_sort | Zhihao Fu |
| collection | DOAJ |
| description | Catch fish optimization algorithm (CFOA) is a newly proposed meta-heuristic algorithm based on human behaviors. CFOA shows better performance on multiple test functions and clustering problems. However, CFOA shows poor performance in some cases, and there is still room for improvement in convergence accuracy, getting rid of local traps, and so on. To further enhance the performance of CFOA, a multi-strategy improved catch fish optimization algorithm (MICFOA) is proposed in this paper. In the exploration phase, we propose a Lévy-based differential independent search strategy to enhance the global search capability of the algorithm while minimizing the impact on the convergence speed. Secondly, in the exploitation phase, a weight-balanced selection mechanism is used to maintain population diversity, enhance the algorithm’s ability to get rid of local optima during the search process, and effectively boost the convergence accuracy. Furthermore, the structure of CFOA is also modified in this paper. A fishermen position replacement strategy is added at the end of the algorithm as a way to strengthen the robustness of the algorithm. To evaluate the performance of MICFOA, a comprehensive comparison with nine other metaheuristic algorithms is performed on the 10/30/50/100 dimensions of the CEC 2017 test functions and the 10/20 dimensions of the CEC2022 test functions. Statistical experiments show that MICFOA has more significant dominance in numerical optimization problems, and its overall performance outperforms the CFOA, PEOA, TLBO, COA, ARO, EDO, YDSE, and other state-of-the-art algorithms such as LSHADE, JADE, IDE-EDA, and APSM-jSO. |
| format | Article |
| id | doaj-art-5c826651ba934b829fef120156f3e3d2 |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2024-08-01 |
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| series | Biomimetics |
| spelling | doaj-art-5c826651ba934b829fef120156f3e3d22025-08-20T01:56:05ZengMDPI AGBiomimetics2313-76732024-08-019950910.3390/biomimetics9090509MICFOA: A Novel Improved Catch Fish Optimization Algorithm with Multi-Strategy for Solving Global ProblemsZhihao Fu0Zhichun Li1Yongkang Li2Haoyu Chen3School of Electronic Information Engineering, Hankou University, Wuhan 430212, ChinaDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, ChinaSchool of Electronic Information Engineering, Hankou University, Wuhan 430212, ChinaCollege of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaCatch fish optimization algorithm (CFOA) is a newly proposed meta-heuristic algorithm based on human behaviors. CFOA shows better performance on multiple test functions and clustering problems. However, CFOA shows poor performance in some cases, and there is still room for improvement in convergence accuracy, getting rid of local traps, and so on. To further enhance the performance of CFOA, a multi-strategy improved catch fish optimization algorithm (MICFOA) is proposed in this paper. In the exploration phase, we propose a Lévy-based differential independent search strategy to enhance the global search capability of the algorithm while minimizing the impact on the convergence speed. Secondly, in the exploitation phase, a weight-balanced selection mechanism is used to maintain population diversity, enhance the algorithm’s ability to get rid of local optima during the search process, and effectively boost the convergence accuracy. Furthermore, the structure of CFOA is also modified in this paper. A fishermen position replacement strategy is added at the end of the algorithm as a way to strengthen the robustness of the algorithm. To evaluate the performance of MICFOA, a comprehensive comparison with nine other metaheuristic algorithms is performed on the 10/30/50/100 dimensions of the CEC 2017 test functions and the 10/20 dimensions of the CEC2022 test functions. Statistical experiments show that MICFOA has more significant dominance in numerical optimization problems, and its overall performance outperforms the CFOA, PEOA, TLBO, COA, ARO, EDO, YDSE, and other state-of-the-art algorithms such as LSHADE, JADE, IDE-EDA, and APSM-jSO.https://www.mdpi.com/2313-7673/9/9/509catch fish optimization algorithmLévy flightweight-balanced selection mechanismglobal optimizationCEC 2018 test suiteCEC 2022 test suite |
| spellingShingle | Zhihao Fu Zhichun Li Yongkang Li Haoyu Chen MICFOA: A Novel Improved Catch Fish Optimization Algorithm with Multi-Strategy for Solving Global Problems Biomimetics catch fish optimization algorithm Lévy flight weight-balanced selection mechanism global optimization CEC 2018 test suite CEC 2022 test suite |
| title | MICFOA: A Novel Improved Catch Fish Optimization Algorithm with Multi-Strategy for Solving Global Problems |
| title_full | MICFOA: A Novel Improved Catch Fish Optimization Algorithm with Multi-Strategy for Solving Global Problems |
| title_fullStr | MICFOA: A Novel Improved Catch Fish Optimization Algorithm with Multi-Strategy for Solving Global Problems |
| title_full_unstemmed | MICFOA: A Novel Improved Catch Fish Optimization Algorithm with Multi-Strategy for Solving Global Problems |
| title_short | MICFOA: A Novel Improved Catch Fish Optimization Algorithm with Multi-Strategy for Solving Global Problems |
| title_sort | micfoa a novel improved catch fish optimization algorithm with multi strategy for solving global problems |
| topic | catch fish optimization algorithm Lévy flight weight-balanced selection mechanism global optimization CEC 2018 test suite CEC 2022 test suite |
| url | https://www.mdpi.com/2313-7673/9/9/509 |
| work_keys_str_mv | AT zhihaofu micfoaanovelimprovedcatchfishoptimizationalgorithmwithmultistrategyforsolvingglobalproblems AT zhichunli micfoaanovelimprovedcatchfishoptimizationalgorithmwithmultistrategyforsolvingglobalproblems AT yongkangli micfoaanovelimprovedcatchfishoptimizationalgorithmwithmultistrategyforsolvingglobalproblems AT haoyuchen micfoaanovelimprovedcatchfishoptimizationalgorithmwithmultistrategyforsolvingglobalproblems |