A Hybrid Cellular Genetic Algorithm for the Traveling Salesman Problem

The traveling salesman problem (TSP), a typical non-deterministic polynomial (NP) hard problem, has been used in many engineering applications. Genetic algorithms are useful for NP-hard problems, especially the problem. However, it some issues solving TSP, including quickly falling into local optimum and an insufficient optimization precision. To address TSP effectively, this paper proposes hybrid Cellular Algorithm with Simulated Annealing (SA) (SCGA). Firstly, SCGA is improved (GA) based on Automata (CA). selection operation performed according to state of cell. Secondly, SCGA, combined SA, introduces elitist strategy improve speed convergence. Finally, proposed algorithm tested against 13 standard benchmark instances from TSPLIB confirm performance three cellular automata rules. experimental results show that, most instances, obtained by using rule 2 better more stable than 1 3. At same time, we compared GA, (CGA) verify SCGA. comparison that distance shortened mean 7% other algorithms, which closer theoretical optimal value good robustness.