The comparison between Simulated Annealing Algorithm and Genetic Algorithm in order to Solve Traveling Salesman Problem in Isfahan Telecommunications Companies

When the complexity of problem is high and could not be solve in appreciate time, the problem can categorize on NP-Hard problems. Meta heuristics algorithms such as Genetic Algorithm, Simulated annealing, Ant Colony Optimization, Tabu search and much more, help researchers to solve NP-Hard problems like Traveling salesman problem. These algorithms would not give the exact best answer; however they can give the optimal or near optimal answers and help researchers to optimize their problems. In the past researches, Genetic Algorithm and Simulated Annealing used to find the best route and the least cost in traveling salesman problems. The main rule of this problem is visiting each city just one time by salesman and try to minimize the total distance between cities. In this work, we solve the traveling salesman problem for 30 telecommunications companies in Isfahan by two Meta heuristics; Genetic Algorithm (discrete and real coding) and Simulated Annealing; and then compare the results of both algorithms and find the better algorithm for our case study.

کلیدواژه ها:

Traveling Salesman Problem (TSP) ، Meta heuristics ، Simulated Annealing Algorithm (SA) ، Genetic Algorithm (GA) ، Isfahan Telecommunications companies