基于求解TSP问题的ACA-GA-PSO算法

为了有效求解TSP问题,提出一种融合蚁群算法、遗传算法、粒子群优化算法思想的混合算法。该算法基于最大-最小蚁群系统框架,在选择下一个城市时采用局部搜索策略避免陷入局部最优,在每次循环结束时用演化交叉策略优化得到的全局最短路径,从而提高求解TSP问题的求解精度及收敛速度。TSPLIB中不同规模的TSP问题的仿真实验结果表明了该算法的有效性与可行性。

ACA-GA-PSO Algorithm for Solving Traveling Salesman Problem

In order to solve the TSP problem effectively,A hybrid algorithm is proposed which combined ideas of ant colony algorithm,genetic algorithms and particle swarm optimization algorithm.The algorithm is based on the Max-Min Ant System,it uses partly search strategy to avoid falling into local optimum when choosing the next city,and using evolution cross strategies to get the global optimal path at the end of each cycle.Solution accuracy and convergence speed was increased.The simulation results of TSP problems with different sizes solving By TSPLIB show it is effective and useful.