MST Ant Colony Optimization with Lin-Kerninghan Local Search for the Traveling Salesman Problem

To solve a typical NP-hard combinatorial optimization problem-traveling salesman problem, ant colony optimization based on minimum spanning tree(MST-ACO), is presented and the performance is reported. The mechanism of MST-ACO is described from three aspects: adopting dual nearest insertion procedure to initialize the pheromone, integrating reinforcement learning through computing lowbound by 1-minimum spanning tree, and combining lin-kerninghan local search. The results clearly show that MST-ACO has the property of effectively guiding the local search heuristics towards promising regions of the search space, which indicates that MST-ACO is an effective approach for solving the traveling salesman problem.