A genetic local search algorithm for solving symmetric and asymmetric traveling salesman problems

The combination of local search heuristics and genetic algorithms is a promising approach for finding near-optimum solutions to the traveling salesman problem (TSP). An approach is presented in which local search techniques are used to find local optima in a given TSP search space, and genetic algorithms are used to search the space of local optima in order to find the global optimum. New genetic operators for realizing the proposed approach are described, and the quality and efficiency of the solutions obtained for a set of symmetric and asymmetric TSP instances are discussed. The results indicate that it is possible to arrive at high quality solutions in reasonable time.