Modified crayfish optimization algorithm with adaptive spiral elite greedy opposition-based learning and search-hide strategy for global optimization DOI Creative Commons
Guanghui Li, Taihua Zhang, Chieh-Yuan Tsai

и другие.

Journal of Computational Design and Engineering, Год журнала: 2024, Номер 11(4), С. 249 - 305

Опубликована: Июль 3, 2024

Abstract Crayfish optimization algorithm (COA) is a novel bionic metaheuristic with high convergence speed and solution accuracy. However, in some complex problems real application scenarios, the performance of COA not satisfactory. In order to overcome challenges encountered by COA, such as being stuck local optimal insufficient search range, this paper proposes four improvement strategies: search-hide, adaptive spiral elite greedy opposition-based learning, competition-elimination, chaos mutation. To evaluate accuracy, speed, robustness modified crayfish (MCOA), simulation comparison experiments 10 algorithms are conducted. Experimental results show that MCOA achieved minor Friedman test value 23 functions, CEC2014 CEC2020, average superiority rates 80.97%, 72.59%, 71.11% WT, respectively. addition, shows applicability progressiveness five engineering actual industrial field. Moreover, 80% 100% rate against on CEC2020 fixed-dimension function benchmark functions. Finally, owns better population diversity.

Язык: Английский

Modified crayfish optimization algorithm with adaptive spiral elite greedy opposition-based learning and search-hide strategy for global optimization DOI Creative Commons
Guanghui Li, Taihua Zhang, Chieh-Yuan Tsai

и другие.

Journal of Computational Design and Engineering, Год журнала: 2024, Номер 11(4), С. 249 - 305

Опубликована: Июль 3, 2024

Abstract Crayfish optimization algorithm (COA) is a novel bionic metaheuristic with high convergence speed and solution accuracy. However, in some complex problems real application scenarios, the performance of COA not satisfactory. In order to overcome challenges encountered by COA, such as being stuck local optimal insufficient search range, this paper proposes four improvement strategies: search-hide, adaptive spiral elite greedy opposition-based learning, competition-elimination, chaos mutation. To evaluate accuracy, speed, robustness modified crayfish (MCOA), simulation comparison experiments 10 algorithms are conducted. Experimental results show that MCOA achieved minor Friedman test value 23 functions, CEC2014 CEC2020, average superiority rates 80.97%, 72.59%, 71.11% WT, respectively. addition, shows applicability progressiveness five engineering actual industrial field. Moreover, 80% 100% rate against on CEC2020 fixed-dimension function benchmark functions. Finally, owns better population diversity.

Язык: Английский

Процитировано

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