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<p>Coati optimization algorithm (COA) as a novel swarm intelligence approach has the advantages of fast convergence speed and high accuracy, but it also has shortages of easy premature and unbalanced searchability. To handle these problems, this paper presents an efficient Multi-Strategies Improved COA (MSICOA), which integrates three strategies: (1) the nonlinear inertia weight factor to control the behaviors of coatis, (2) the Harris Hawk besieging mechanism from Harris Hawks Optimization (HHO) to balance the ability of coatis between local and global searchability, and (3) the sparrow vigilant mechanism in the Sparrow Search Algorithm (SSA) to improve the vigilance ability of the coatis to search in the search domain and accelerate the convergence speed. Computational complexity analysis confirms that the proposed MSICOA has identical computational complexity with COA theoretically. Comprehensive numerical experiments in CEC2017 and CEC2020 against twelve well-known optimizers demonstrate the superiority of MSICOA, and ablation experiments validate the independent contribution of integrated three strategies. Finally, MSICOA is employed to solve six engineering problems, and the experimental results show that the proposed MSICOA has remarkable convergence accuracy, robustness, and practicality. The source code of this research can be downloaded at <a href="https://github.com/RuiZhong961230/MSICOA">https://github.com/RuiZhong961230/MSICOA</a>.</p>

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Multi-strategies improved coati optimization algorithm and performance analysis

  • 期刊论文

  • 发表日期：

    2025年8月22日

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  • Chunqing Li,
  • Zhongmin Wang,
  • Jun Yu,
  • Mahmoud Abdel-Salam,
  • Essam H. Houssein,
  • Rui Zhong

摘要

Coati optimization algorithm (COA) as a novel swarm intelligence approach has the advantages of fast convergence speed and high accuracy, but it also has shortages of easy premature and unbalanced searchability. To handle these problems, this paper presents an efficient Multi-Strategies Improved COA (MSICOA), which integrates three strategies: (1) the nonlinear inertia weight factor to control the behaviors of coatis, (2) the Harris Hawk besieging mechanism from Harris Hawks Optimization (HHO) to balance the ability of coatis between local and global searchability, and (3) the sparrow vigilant mechanism in the Sparrow Search Algorithm (SSA) to improve the vigilance ability of the coatis to search in the search domain and accelerate the convergence speed. Computational complexity analysis confirms that the proposed MSICOA has identical computational complexity with COA theoretically. Comprehensive numerical experiments in CEC2017 and CEC2020 against twelve well-known optimizers demonstrate the superiority of MSICOA, and ablation experiments validate the independent contribution of integrated three strategies. Finally, MSICOA is employed to solve six engineering problems, and the experimental results show that the proposed MSICOA has remarkable convergence accuracy, robustness, and practicality. The source code of this research can be downloaded at https://github.com/RuiZhong961230/MSICOA.