Network Center, Sanming University, Sanming 365004, China.
School of Information and Engineering, Sanming University, Sanming 365004, China.
Comput Intell Neurosci. 2022 Apr 30;2022:4673665. doi: 10.1155/2022/4673665. eCollection 2022.
One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems. Firstly, we introduce tent chaotic map in the initialization stage to improve the diversity of the initialization population. Secondly, an exploration factor is proposed to optimize parameters for improving the ability of exploration. Finally, a random walk strategy is proposed to enhance the exploitation capability of HHO further and help search agent jump out the local optimal. Results from systematic experiments conducted on 23 benchmark functions and the CEC2017 test functions demonstrated that the proposed method can provide a more reliable solution than other well-known algorithms.
最受欢迎的基于人群的元启发式算法之一是哈里斯鹰优化（HHO），它模仿了自然界中哈里斯鹰的狩猎机制。虽然 HHO 可以为特定问题找到最优解，但它在局部最优解中停滞不前。在本文中，提出了一种名为 ERHHO 的改进哈里斯鹰优化算法，用于解决全局优化问题。首先，我们在初始化阶段引入帐篷混沌映射来提高初始化种群的多样性。其次，提出了一个探索因子来优化参数，以提高探索能力。最后，提出了一种随机游走策略，进一步增强 HHO 的开发能力，帮助搜索代理跳出局部最优。在 23 个基准函数和 CEC2017 测试函数上进行的系统实验结果表明，与其他知名算法相比，该方法可以提供更可靠的解决方案。
