Research On Ant Colony Optimization With Tabu Search Ability

TSP( traveling salesman problem) is a classical problem in combinatorial optimization, it's not totally be solved, the route number and the number of cities is increased exponently, so we couldn't find the best solution easily.Ant Algorithm (AA) is a hotspot in the research of the meta-heuristic algorithms, and from it was introduced, it has been used to solve all kinds of complex optimization problem. As a global searching approach, AA has some characteristic, such as positive feedback, distributing, paralleling, self- organizing, etc. But AA also has many shortcomings, such as slow convergence and being premature. In order to improve its capability, 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 algorithms'performance is meliorated.Firstly, aiming at solving AA's slow convergence, we 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'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.