融合扰动策略的自适应哈里斯鹰优化算法  

Adaptive Harris Hawks Optimization Algorithm Based on Disturbance Strategies

作　　者：尚凯凯 SHANG Kaikai(School of Civil Engineering,Hebei University of Engineering,Handan 056038)

机构地区：[1]河北工程大学土木工程学院,邯郸056038

出　　处：《计算机与数字工程》2025年第2期338-346,共9页Computer & Digital Engineering

摘　　要：为提高哈里斯鹰优化算法收敛精度和跳出局优的能力,提出了融合扰动策略的自适应哈里斯鹰优化算法(ADHHO)。首先,通过改进Tent混沌映射产生更加均匀的种群,保证种群的多样性。其次,引入非线性逃逸能量函数策略,平衡算法局部开发和全局搜索的性能。然后,通过自适应扰动对最优解进行变异扰动,避免算法进入局部最优,提升算法的反早熟能力。最后,将ADHHO对八个基准测试函数进行仿真实验,并与群体智能优化算法和改进的HHO进行对比求解分析。结果表明,所提算法在收敛精度和反早熟能力方面具有一定优势。In order to improve the convergence accuracy of Harris hawks optimization algorithm and the ability of jumping out of the local optimization,an adaptive Harris hawks optimization algorithm based on disturbance strategies is proposed.Firstly,the diversity of the population is guaranteed by improving Tent chaotic map to produce more uniform population.Secondly,the nonlin⁃ear escape energy function strategy is introduced to balance the performance of local development and global search.Then,the opti⁃mal solution is mutated and disturbed by adaptive disturbance to avoid the premature phenomenon of the algorithm and improve the ability of the algorithm to jump out of local extremum.Finally,the ADHHO is used to simulate eight benchmark functions,and swarm intelligence optimization algorithms and improved HHO are compared for solution analysis.The results show that the pro⁃posed algorithm has certain advantages in convergence accuracy and anti-premature ability.

关 键 词：哈里斯鹰优化算法 Tent混沌 非线性逃逸能量 自适应扰动 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]