Research on TSP Application Based on Improved Ant Colony Algorithm
Abstract— In order to solve the shortcomings of traditional ant colony algorithm in solving traveling
salesman problem (TSP), such as slow convergence speed and easy to fall into local optimum, an improved
ant colony algorithm (IACO) is proposed. The algorithm uses k-nearest neighbor to influence the distribution
of initial pheromones, applies roulette operator to urban transfer rules, and improves the pheromone updating
strategy of ant colony to accelerate the convergence speed and improve the optimization ability of algorithm.
Taking chn31 city problem as an example, the computer simulation results show that the improved algorithm
is an optimization algorithm which can accelerate the convergence speed and improve the optimization
ability, and is effective for solving TSP.
Index Terms— Ant colony algorithm, Traveling Salesman Problem, Pheromone, Roulette
Pan Zhao, Xiaoqin Ma, Xiaoling Yin
College of Mathematics and Computer Science, Chizhou University , CHINA
Cite: Pan Zhao, Xiaoqin Ma, Xiaoling Yin, "Research on TSP Application Based on Improved Ant Colony Algorithm," Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering, pp. 420-426, Hong Kong, 15-17 June, 2019.