Simulated Annealing with Probabilistic Analysis for Solving Traveling Salesman Problems

Simulated Annealing (SA) is a widely used meta-heuristic that was inspired from the annealing process of re-crystallization of metals. Therefore, the efficiency of SA is highly affected by the annealing schedule. As a result, in this paper, we presented an empirical work to provide a comparable annealing schedule to solve symmetric traveling salesman problems (TSP). Randomized complete block design is also used in this study. The results show that different parameters do affect the efficiency of SA and thus, we propose the best found annealing schedule based on the Post Hoc test. SA was tested on seven selected benchmarked problems of symmetric TSP with the proposed annealing schedule. The performance of SA was evaluated empirically alongside with benchmark solutions and simple analysis to validate the quality of solutions. Computational results show that the proposed annealing schedule provides a good quality of solution.