Pei Shengyu, Sun Gang, Tong Lang
School of Artiffcal Intelligence, Guangxi Minzu University, Nanning, Guangxi, China.
Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi Minzu University, Nanning, Guangxi, China.
PeerJ Comput Sci. 2025 May 29;11:e2901. doi: 10.7717/peerj-cs.2901. eCollection 2025.
This study proposes an improved hippopotamus optimization algorithm to address the limitations of the traditional hippopotamus optimization algorithm in terms of convergence performance and solution diversity in complex high-dimensional problems. Inspired by the natural behavior of hippopotamuses, this article introduces chaotic map initialization, an adaptive exploitation mechanism, and a solution diversity enhancement strategy based on the original algorithm. The chaotic map is employed to optimize the initial population distribution, thereby enhancing the global search capability. The adaptive exploitation mechanism dynamically adjusts the weights between the exploration and exploitation phases to balance global and local searches. The solution diversity enhancement is achieved through the introduction of nonlinear perturbations, which help the algorithm avoid being trapped in local optima. The proposed algorithm is validated on several standard benchmark functions (CEC17, CEC22), and the results demonstrate that the improved algorithm significantly outperforms the original hippopotamus optimization algorithm and other mainstream optimization algorithms in terms of convergence speed, solution accuracy, and global search ability. Moreover, statistical analysis further confirms the superiority of the improved algorithm in balancing exploration and exploitation, particularly when dealing with high-dimensional multimodal functions. This study provides new insights and enhancement strategies for the application of the hippopotamus optimization algorithm in solving complex optimization problems.
本研究提出了一种改进的河马优化算法，以解决传统河马优化算法在复杂高维问题中收敛性能和解多样性方面的局限性。受河马自然行为的启发，本文在原算法的基础上引入了混沌映射初始化、自适应开发机制和解决方案多样性增强策略。混沌映射用于优化初始种群分布，从而提高全局搜索能力。自适应开发机制动态调整探索和开发阶段之间的权重，以平衡全局搜索和局部搜索。通过引入非线性扰动实现解决方案多样性增强，这有助于算法避免陷入局部最优。所提出的算法在几个标准基准函数（CEC17、CEC22）上进行了验证，结果表明改进后的算法在收敛速度、解精度和全局搜索能力方面显著优于原始河马优化算法和其他主流优化算法。此外，统计分析进一步证实了改进算法在平衡探索和开发方面的优越性，特别是在处理高维多峰函数时。本研究为河马优化算法在解决复杂优化问题中的应用提供了新的见解和增强策略。
