改进遗传模拟退火算法在TSP优化中的应用  

Application of improved genetic simulated annealing algorithm in TSP optimization

作　　者：何庆 吴意乐 徐同伟 

机构地区：[1]贵州大学大数据与信息工程学院,贵阳550025

出　　处：《控制与决策》2018年第2期219-225,共7页Control and Decision

基　　金：贵州省科技厅基金项目(黔科合LH字[2014]7628);贵州省教育厅青年科技人才成长项目(黔教合KY字[2016]124);贵州大学博士基金项目(贵大人基合字[2010]010)

摘　　要：针对旅行商问题(TSP)优化中,遗传算法(GA)容易陷入局部最优、模拟退火算法(SA)收敛速度慢的问题,提出一种基于改进遗传模拟退火算法(IGSAA)的TSP优化算法.首先根据优化目标建立数学模型;然后对遗传算法部分中的适应度函数、交叉变异算子进行改进,使算法能够更加有效地避免陷入局部最优;最后根据旧种群和新种群每个对应个体的进化程度提出一种改进自适应的Metropolis准则,使模拟退火算法部分的染色体跳变更具有自适应性,利于算法寻优.对不同TSP实例的实验结果表明,与其他路径优化算法优化结果相比,所提出的IGSAA算法能够对不同TSP实例优化得到更优的旅行路径.Aiming at the problem that the genetic algorithm（GA） is easy to fall into local optimum and the simulated annealing algorithm（SA） convergence rate is slow in the optimization of traveling salesman problem（TSP）, a TSP optimization algorithm based on the improved genetic simulated annealing algorithm（IGSAA） is proposed. Firstly, the mathematical model is established according to the optimization goal. Then, the fitness function, crossover and mutation operators are improved in the part of genetic algorithm in order to make the algorithm more efficient to avoid falling into local optimum. Finally,an improved adaptive Metropolis criterion is proposed according to the evolution degree of each corresponding individual between old and new population in order to make the jump changing more adaptive in the part of simulation annealing algorithm which is propitious to the optimization of the algorithm. The experimental results on different TSP instances show that, the IGSAA algorithm designed can obtain better travel path on optimizing different TSP instances compared with other path optimization algorithms.

关 键 词：旅行商问题 遗传算法 模拟退火算法 交叉变异算子 METROPOLIS准则 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]