改进蝴蝶优化算法求解TSP问题  

Improved butterfly optimization algorithm for solving TSP problems

作　　者：张小萍[1] 李相成 ZHANG Xiaoping;LI Xiangcheng(College of Computer and Electronic Information,Guangxi University,Nanning 530004,China)

机构地区：[1]广西大学计算机与电子信息学院,广西南宁530004

出　　处：《河南科技学院学报(自然科学版)》2025年第1期51-57,共7页Journal of Henan Institute of Science and Technology(Natural Science Edition)

基　　金：国家自然科学基金(61962005)。

摘　　要：目的蝴蝶优化算法(BOA)是近年提出的一种新型元启发式群智能算法,但其对旅行商问题(TSP)这类组合优化问题求解时,存在寻优精度不足、早熟停滞等问题.为此,利用四种混合策略提出改进的蝴蝶优化算法,以更有效求解TPS问题.方法使用自适应参数控制平衡全局搜索和局部搜索过程;将粒子群优化算法更新公式与BOA结合,提升算法全局优化性能;采用Metropolis原则提高BOA跳出局部最优解的性能;利用3-opt策略提升算法的局部优化性能.结果仿真实验结果表明,改进的BOA算法比其他四个对比算法具有较高的寻优精度和更小的误差率.结论改进的BOA算法可以避免早熟停滞、减小误差率,能有效求解TSP问题.Objective The Butterfly Optimization Algorithm(BOA)is a new type of meta heuristic swarm intelligence algorithm proposed in recent years.However,when solving combinatorial optimization problems such as Traveling Salesman Problem(TSP),it faces problems such as insufficient optimization accuracy and premature convergence and stagnation.To this end,an improved butterfly optimization algorithm is proposed using four hybrid strategies.Methods An adaptive parameter control is used to balance the global search and local search processes.Combining the updated formula of particle swarm optimization algorithm with BOA to improve the global optimization performance of the algorithm.The Metropolis principle is adopted to improve the performance of BOA in jumping out of local optima.Using the 3-opt strategy to improve the local optimization performance of the algorithm.Results Simulation experiments show that the improved BOA algorithm has high optimization accuracy and smaller error rate than the other four compared algorithms.Conclusion Therefore,proposed algorithm can avoid premature stagnation,reduce error rates,and effectively solve TSP problem.

关 键 词：旅行商问题 蝴蝶优化算法 3-opt策略 Metropolis原则 粒子群优化算法 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]