Han Tao, Wang Haiyan, Li Tingting, Liu Quanzeng, Huang Yourui
School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China.
Biomimetics (Basel). 2025 Feb 5;10(2):90. doi: 10.3390/biomimetics10020090.
The hippopotamus optimization algorithm (HO) is a novel metaheuristic algorithm that solves optimization problems by simulating the behavior of hippopotamuses. However, the traditional HO algorithm may encounter performance degradation and fall into local optima when dealing with complex global optimization and engineering design problems. In order to solve these problems, this paper proposes a modified hippopotamus optimization algorithm (MHO) to enhance the convergence speed and solution accuracy of the HO algorithm by introducing a sine chaotic map to initialize the population, changing the convergence factor in the growth mechanism, and incorporating the small-hole imaging reverse learning strategy. The MHO algorithm is tested on 23 benchmark functions and successfully solves three engineering design problems. According to the experimental data, the MHO algorithm obtains optimal performance on 13 of these functions and three design problems, exits the local optimum faster, and has better ordering and stability than the other nine metaheuristics. This study proposes the MHO algorithm, which offers fresh insights into practical engineering problems and parameter optimization.
河马优化算法（HO）是一种通过模拟河马行为来解决优化问题的新型元启发式算法。然而，传统的HO算法在处理复杂的全局优化和工程设计问题时可能会遇到性能下降并陷入局部最优。为了解决这些问题，本文提出了一种改进的河马优化算法（MHO），通过引入正弦混沌映射来初始化种群、改变增长机制中的收敛因子以及纳入小孔成像反向学习策略，以提高HO算法的收敛速度和求解精度。MHO算法在23个基准函数上进行了测试，并成功解决了三个工程设计问题。根据实验数据，MHO算法在其中13个函数和三个设计问题上获得了最优性能，更快地跳出局部最优，并且比其他九种元启发式算法具有更好的排序性和稳定性。本研究提出了MHO算法，为实际工程问题和参数优化提供了新的见解。
