• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2023, Vol. 45 ›› Issue (09): 1648-1660.

• 人工智能与数据挖掘 • 上一篇    下一篇

多策略改进的混沌哈里斯鹰优化算法

胡春安,熊昱然   

  1. (江西理工大学信息工程学院,江西 赣州 341000)

  • 收稿日期:2022-02-25 修回日期:2022-10-09 接受日期:2023-09-25 出版日期:2023-09-25 发布日期:2023-09-12
  • 基金资助:
    国家重点研发计划(2018YFC1504705);国家自然科学基金(41562019,11461031)

A multi-strategy improved chaotic Harris hawk optimization algorithm

HU Chun-an,XIONG Yu-ran   

  1. (School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou  341000,China)
  • Received:2022-02-25 Revised:2022-10-09 Accepted:2023-09-25 Online:2023-09-25 Published:2023-09-12

摘要: 哈里斯鹰优化(HHO)算法是近期提出的一种元启发式算法,模拟了生物性的种群捕食调度。针对哈里斯鹰优化算法开发能力不足、种群多样性下降和容易陷入局部最优等缺点,提出了一种多策略改进的哈里斯鹰优化算法(MHHO)。首先,在哈里斯鹰中引入混沌局部搜索策略,利用混沌映射的优点,围绕当前个体进行局部搜索,从而找到更好的个体,提高算法的开发能力。其次,为了增强种群多样性,提出了精英备选池策略。此外,通过对优势种群信息的采样来更好地引导种群进化方向,采用分布估计策略提高算法收敛效率。CEC2017测试实验结果表明,改进后的算法兼顾了收敛速度与全局搜索等能力,最后将算法用于求解工程约束问题,证明了改进后的算法的实用性。

关键词: 哈里斯鹰优化算法, 分布估计策略, 混沌局部搜索, 工程约束问题

Abstract: The Harris Hawk Optimization algorithm (HHO) is a recently proposed meta-heuristic algorithm that simulates biological population predation scheduling in the original hawk algorithm design. A Multi-strategy improved Harris Hawk Optimization algorithm (MHHO) is proposed to address the shortcomings of the Harris Hawk Optimization algorithm such as insufficient exploitation capability, decreasing population diversity, and easily falling into local optimality. Firstly, a chaotic local search strategy is introduced into the Harris Hawk to improve the exploitation ability of the algorithm. The advantages of chaotic mapping are exploited to find better individuals by performing local search around the current individual. Secondly, to enhance the population diversity, an elite alternative pooling strategy is proposed. In addition, the distribution estimation strategy is used to improve the convergence efficiency of the algorithm by sampling the dominant population information to better guide the direction of population evolution. Experimental tests on CEC2017 demonstrate that the improved algorithm achieves a balance between convergence speed and global search ability. Finally, the practicality of the improved algorithm is demonstrated by applying it to solve engineering constrained problems.

Key words: harris hawk optimization algorithm (HHO), distribution estimation strategy, chaotic local search, engineering constraints problem