A Fast Ant Colony Optimization Algorithm for Traveling Salesman Problems

Ant colony optimization (ACO) is a population-based metaheuristic technique to solve combination optimization problems effectively, such as traveling salesman problem (TSP), multidimensional knapsack problem (MKP), and so on. 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. Combining the pheromone updating with an improvement of the stochastic search strategy, 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. Finally, a simple mutation strategy with lower computational complexity is adopted to optimize the evolution result. Experimental results on different benchmark data sets show that the proposed algorithm can not only get better optimal solutions but also enhance greatly the convergence speed.