The traveling salesman problem is one of the most notoriously intractable NP-complete optimization problems. Over the last 10 years, simulated annealing and tabu search have emerged as an effective algorithm for the TSP. However, the quality of solutions found by using tabu search approach depends on the initial solution and the iteration process of simulated annealing is slow. To overcome this problem and provide an efficient methodology for the TSP, the heuristic search approach based on simulated annealing which combining tabu search strategy and two neighborhood perturbation factor is developed. The proposed hybrid algorithm is tested on standard benchmark sets and compared with the conventional simulated annealing algorithm. The computational results show that the proposed algorithm has significantly better convergence speed compared with conventional simulated annealing algorithm and can obtain high-quality solutions within reasonable computing times.
['Yi Liu', 'Shengwu Xiong', 'Hongbing Liu']
Hybrid simulated annealing algorithm based on adaptive cooling schedule for TSP