TSP (traveling salesman problem) is a classical problem in combinatorial optimization. It's not totally solved; the route number and the number of cities has increased exponentially, so we couldn't find the best solution easily. This paper does a lot research of tabu search (TS) besides AA and proposes a new algorithm. Making use of TS's advantages, the new proposed algorithm's performance is meliorated. Firstly, aiming at solving AA's slow convergence, the authors increase the pheromone of the best route, decrease the pheromone of the worst route, to increase the conductive ability of the pheromone to the algorithm. Secondly, aiming at solving AA's being premature, this paper introduces TS into AS's every iteration. The TS can help the algorithm find a better solution. So, the new algorithm's convergence speed is quickened, and its performance is improved. At last, this paper applied the algorithm to the traveling salesman problem to test its performances. The simulation results show that the new algorithm could find optimum solutions more effectively in time and quantity.
DEVELOPMENT OF THE PARALLEL ANT COLONY OPTIMIZATION ALGORITHM BASED ON THE TRAVELING SALESMAN PROBLEM