Improved Ant Colony Optimization for the Traveling Salesman Problem

The traveling salesman problem (TSP) in operations research is a classical problem in discrete or combinatorial optimization. It is a prominent illustration of a class of problems in computational complexity theory which are classified as NP-hard. Ant colony optimization inspired by co-operative food retrieval have been widely applied unexpectedly successful in the combinatorial optimization. This paper presents an improved ant colony optimization algorithm for traveling salesman problem, which adopts a new probability selection mechanism by using Held-Karp lower bound to determine the trade-off between the influence of the heuristic information and the pheromone trail. The experiments showed that it can stably generate better solution for the traveling salesman problem than rank-based ant system and max-min ant colony optimization algorithm.