A new Ant Colony Algorithm for solving Traveling Salesman Problem
Ant colony optimization (ACO) is a population-based metaheuristic technique to solve combination optimization problems effectively. However, how to improve the performance of ACO algorithms is still an active research topic. Though there are many algorithms solving TSPs effectively, there is an application bottleneck that the ACO algorithm costs too much time in order to get an optimal solution. This paper revised pheromones in local and global update mode. a fast ACO algorithm for solving TSPs is presented in this paper. Firstly, a new pheromone increment model called ant constant, which keeps energy conversation of ants, is introduced to embody the pheromone difference of different candidate paths. Meanwhile, a pheromone diffusion model, which is based on info fountain of a path, is established to reflect the strength field of the pheromone diffusion faithfully, and it strengthens the collaboration among ants. Experimental results on different benchmark data sets show that the proposed algorithm can not only get better optima solutions but also enhance greatly the convergence speed.
TSP ACO Combinatorial optimization problem
Wei Zhao Xingsheng Cai Ying Lan
College of Information Technology JiLin Agriculture University Changchun China
国际会议
杭州
英文
530-533
2012-03-23（万方平台首次上网日期，不代表论文的发表时间）