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.