A parallel genetic algorithm with edge assembly crossover for 100,000-city scale TSPs

In this paper, we propose a new parallel genetic algorithm (GA) with edge assembly crossover (EAX) for the traveling salesman problem (TSP). GA with EAX (GA-EAX) is one of the promising meta-heuristics for TSP and found best-known tours for several well-known 100,000-city scale TSP instances. However, it takes about ten days to execute this GA just one time using the default configuration on the 120,000-city instance 1. Therefore, it is crucial to reduce the running time of GA-EAX for 100,000-city scale instances in order to make it possible to improve the algorithm through trial and error. The proposed parallel GA achieves about twenty-times speed up without deteriorating the quality of solutions compared to the original GA-EAX. We also demonstrate that the proposed parallel GA successfully finds new best-known tours for the 120,000-city and 180,000-city instances called vangogh120K and courbet180K, respectively.