摘要
针对徒步旅行优化算法(HOA)存在收敛速度慢、全局搜索和局部开发能力不平衡等问题,提出一种多策略徒步旅行优化算法(MSHOA)。首先,采用Chebyshev混沌映射初始化提高种群质量;其次,融入自适应扩张因子策略提高算法收敛速度;再次,引入部分维度重组与突变策略增强算法跳出局部极值的能力。将MSHOA与粒子群算法(PSO)、鲸鱼优化算法(WOA)、哈里斯鹰优化算法(HHO)、飞蛾扑火优化算法(MFO)在12个基准测试函数上进行仿真实验。结果表明,MSHOA相较于其他优化算法寻优精度更高、收敛速度更快。最后,将MSHOA应用于减速器设计和焊接梁设计问题。实验结果表明,该算法相较于标准HOA具有显著优势,验证了其在求解实际应用问题中的可行性。
A multi-strategy hiking optimization algorithm(MSHOA)is proposed in this paper to ad-dress the limitations of slow convergence speed and imbalanced global-local search capabilities in the original hiking optimization algorithm(HOA).Initially,Chebyshev chaotic mapping is employed to en-hance population initialization quality.Subsequently,an adaptive expansion factor strategy is incorpo-rated to accelerate convergence speed.Thirdly,a partial dimension recombination and mutation strat-egy is introduced to strengthen the algorithm's ability to escape local optima.Comprehensive simula-tion experiments are conducted on 12 benchmark functions comparing MSHOA with particle swarm op-timization(PSO),whale optimization algorithm(WOA),Harris hawks optimization(HHO),and moth-flame optimization(MFO).Results demonstrate that MSHOA achieves superior optimization accuracy and faster convergence compared to other algorithms.Finally,engineering validation through speed reducer design and welded beam design applications confirms that MSHOA exhibits signifi-cant performance advantages over standard HOA,proving its effectiveness in solving real-world en-gineering problems.
作者
潘博阳
PAN Boyang(PAP Command College,Tianjin 300250,China)
出处
《信息工程大学学报》
2025年第3期275-281,共7页
Journal of Information Engineering University
作者简介
潘博阳(1987-),男,讲师,硕士,主要研究方向为智能优化算法。E-mail:panboyang@126.com。
