Modified ant colony optimization with improved tour construction and pheromone updating strategies for traveling salesman problem

Abstract

To improve ant colony optimization (ACO) for traveling salesman problem (TSP), its two main strategies which are tour construction and pheromone updating have been modified, and one modified ACO (MACO) has been proposed. For the first strategy (tour construction strategy), one new method to construct tours by combining paths of two meeting ants has been applied. And for second strategy (pheromone updating strategy), one new method to polarize pheromone density of all paths has been proposed. Based on the applications for 40 standard benchmark TSP instances (datasets) ranging from 29 to 13,509 cities, the good performance of the MACO is verified. To verify the MACO deeply, based on the applications for some standard TSP instances, the computing results of MACO are compared with three typical state-of-the-art algorithms based on ACO methods. Moreover, based on the applications for some standard TSP instances, the computing results of MACO are compared with 10 state-of-the-art metaheuristic algorithms. The comparison studies show that the MACO can attain the optimal solution with higher accuracy, no matter how complicated the TSPs are. And its performance is the best, comparing to all state-of-the-art algorithms. At last, two new strategies used in MACO have been analyzed comprehensively by the applications for 25 TSPs. The results show that the first strategy is mainly used to improve the computing efficiency, and the second one is mainly used to improve the computing effect.