Wu Xingtao, Ding Yunfei, Wang Lin, Zhang Hongwei
School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China.
Amerson Biomedical (Shanghai) Co., Ltd., Shanghai 201318, China.
Biomimetics (Basel). 2025 May 16;10(5):323. doi: 10.3390/biomimetics10050323.
Optimization algorithms serve as a powerful instrument for tackling optimization issues and are highly valuable in the context of engineering design. The coati optimization algorithm (COA) is a novel meta-heuristic algorithm known for its robust search capabilities and rapid convergence rate. However, the effectiveness of the COA is compromised by the homogeneity of its initial population and its reliance on random strategies for prey hunting. To address these issues, a multi-strategy adaptive coati optimization algorithm (MACOA) is presented in this paper. Firstly, Lévy flights are incorporated into the initialization phase to produce high-quality initial solutions. Subsequently, a nonlinear inertia weight factor is integrated into the exploration phase to bolster the algorithm's global search capabilities and accelerate convergence. Finally, the coati vigilante mechanism is introduced in the exploitation phase to improve the algorithm's capacity to escape local optima. Comparative experiments with many existing algorithms are conducted using the CEC2017 test functions, and the proposed algorithm is applied to seven representative engineering design problems. MACOA's average rankings in the three dimensions (30, 50, and 100) were 2.172, 1.897, and 1.759, respectively. The results show improved optimization speed and better performance.
优化算法是解决优化问题的有力工具，在工程设计领域具有很高的价值。浣熊优化算法（COA）是一种新颖的元启发式算法，以其强大的搜索能力和快速的收敛速度而闻名。然而，COA的有效性受到其初始种群同质性以及依赖随机策略进行猎物搜索的影响。为了解决这些问题，本文提出了一种多策略自适应浣熊优化算法（MACOA）。首先，在初始化阶段引入莱维飞行以产生高质量的初始解。随后，在探索阶段引入非线性惯性权重因子以增强算法的全局搜索能力并加速收敛。最后，在开发阶段引入浣熊警戒机制以提高算法逃离局部最优的能力。使用CEC2017测试函数与许多现有算法进行了对比实验，并将所提出的算法应用于七个具有代表性的工程设计问题。MACOA在三个维度（30、50和100）上的平均排名分别为2.172、1.897和1.759。结果表明优化速度有所提高且性能更好。
