摘要
为解决蛇形优化算法收敛速度慢和寻优能力差的问题,提出一种基于改进的Tent混沌映射和莱维飞行扰动的先进蛇形优化算法(ASO)。引入改进Tent混沌映射,提高初始种群的多样性;在蛇群勘探和开发过程中引入自适应概率阈值,平衡算法全局搜索和局部开发的能力;为保证种群进化方向,将莱维飞行扰动和贪心算法相结合,对劣势蛇个体生成更大扰动。通过在9个复杂测试函数上与其它4种元启发式算法进行对比,实验结果表明,先进蛇形优化算法在收敛速度、求解精度以及稳定性方面有较大提高,通过Wilcoxon秩和检验证明ASO与其它算法有明显不同。将ASO算法用于求解弹簧设计优化问题。
To solve the problems of low convergence speed and poor optimization ability of snake optimization algorithms,an advanced snake optimization algorithm(ASO)based on improved Tent chaotic mapping and Levy flight disturbance was proposed.An improved Tent chaotic map was introduced to enhance the diversity of the initial population.In the exploration and development process of snake swarms,adaptive probability thresholds were introduced to balance the algorithm’s global search and local development capabilities.To ensure the direction of population evolution,the Levy flight disturbance and greedy algorithm were combined to generate larger disturbances for disadvantaged snake individuals.By comparing with the other four meta heuristic algorithms on 9 complex test functions,experimental results show that the advanced snake optimization algorithm has significant improvements in convergence speed,solution accuracy,and stability.The Wilcoxon rank sum test proves that ASO is significantly different from other algorithms.The ASO algorithm was applied to solve the spring design optimization problem.
作者
程彦琳
李书琴
CHENG Yan-lin;LI Shu-qin(College of Information Engineering,Northwest A&F University,Yangling 712100,China)
出处
《计算机工程与设计》
北大核心
2024年第9期2658-2668,共11页
Computer Engineering and Design
基金
国家重点研发计划基金项目(2022YFD1300201)。
关键词
蛇形优化算法
混沌映射
自适应阈值
莱维飞行
贪心算法
变异扰动
元启发式算法
snake optimization algorithm
chaotic mapping
adaptive threshold
Levy flight
greedy algorithm
variation distur-bance
meta-heuristic algorithm
