基于T-ACO算法的旅行商问题求解优化研究

Optimization of Traveling Salesman Problem Solution Based on T-ACO Algorithm

为了有效求解旅行商问题,本文提出了一种基于T分布的改进蚁群算法。针对基本蚁群算法易陷入局部最优、寻优精度低等缺陷,在优化过程中,在信息素更新原则上,引入T分布,有益于基本蚁群算法弥补其不足。在基本蚁群算法中增加了信息素的突变,使得蚂蚁群的多样性提高,从而跳出局部最优的限制。与此同时,T-ACO算法在旅行商问题搜寻精度与收敛速度方面也得到了提高。对T-ACO求解旅行商问题的性能进行了实验仿真,实验分析表明,T-ACO算法有更好的寻优能力。

In order to effectively solve the traveling salesman problem,this paper proposes an improved ant colony algorithm based on T distribution.Aiming at solving the defects that the basic ant colony algorithm easily falls into local optimum and has low optimization precision,T-distribution is introduced in the optimization process based on the principle of pheromone update,which helps to make up for shortcomings of basic ant colony algorithm.The mutation of pheromone is added to the basic ant colony algorithm in order to improve the diversity of ant colony,so that the basic ant colony algorithm can jump out of the local optimal limit.At the same time,T-ACO algorithm improves the search accuracy and convergence speed of solving the traveling salesman problem.Experimental analysis shows that T-ACO algorithm has better optimization capability.