Traveling salesman problem (TSP) is a combinatorial optimization problem. A new ant evolution algorithm to resolve TSP problem is proposed in this paper. Based on the latest achievement of research on actual ants, the algorithm first takes a set of Pareto optimal solution, which is obtained by scout ants using nearest-neighbor search and diffluence strategy, as the initial population. Then the operators of genetic algorithm, including self-adaptive crossover, mutation and inversion which have the strong local search ability, to speed up the procedure of optimization. Consequently, the optimal solution is obtained relatively fast. The experimental results showed that, the algorithm proposed in this paper is characterized by fast convergence, and can achieve better optimization results.
['Qingbao Zhu', 'Shu-Yan Chen']
A New Ant Evolution Algorithm to Resolve TSP Problem