基于精英引导的改进哈里斯鹰优化算法  

An improved Harris hawks optimization algorithm based on elite guidance

作　　者：李雨恒 高尚 孟祥宇 LI Yu-heng;GAO Shang;MENG Xiang-yu(School of Computer,Jiangsu University of Science and Technology,Zhenjiang 212100,China)

机构地区：[1]江苏科技大学计算机学院,江苏镇江212100

出　　处：《计算机工程与科学》2024年第2期363-373,共11页Computer Engineering & Science

基　　金：国家自然科学基金(62176107)。

摘　　要：针对哈里斯鹰优化算法(HHO)易陷入局部最优和收敛速度慢的问题,提出一种基于精英引导的改进哈里斯鹰优化算法(EHHO)。首先,引入精英反向学习,以精英中心为对称中心进行反向学习来优化种群结构,增强算法跳出局部最优的能力;其次,引入精英演化策略,以精英个体为主体进行基于高斯随机突变的演化来提升种群质量,加快算法收敛速度;最后,引入自适应机制,动态调整精英演化策略中2种演化方式的选择概率,以提升算法稳定性。为验证改进算法的有效性,选取15个基准函数进行仿真实验。实验结果表明,改进算法在寻优性能和鲁棒性上均有明显提升,在优化算法中具有一定竞争力。Aiming at the problems that Harris Hawks Optimization(HHO)is easy to fall into local optimization and has slow convergence speed,an improved Harris Hawks Optimization algorithm based on elite guidance(EHHO)is proposed.Firstly,elite opposite learning is introduced,and the elite center is used as the symmetrical center for opposite learning to optimize the population structure and enhance the ability of the algorithm to jump out of local optimum.Secondly,the elite evolution strategy is introduced,and the evolution based on Gaussian random mutation is carried out with elite individuals as the main body to improve the quality of the population and improve the convergence speed of the algorithm.Finally,an adaptive mechanism is introduced to dynamically adjust the selection probability of the two evolution modes in the elite evolution strategy to improve the stability of the algorithm.To verify the effectiveness of the improved algorithm,15 benchmark functions are selected for simulation experiments.The experimental results show that the improved algorithm has obvious improvement in optimization performance and robustness,and has certain competitiveness in optimization algorithms.

关 键 词：哈里斯鹰优化算法 精英反向学习 精英演化策略 高斯随机突变 自适应机制 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]