Research on an Improved Ant Colony Optimization Algorithm and its Application

Abstract

In order to improve the global solving ability and convergence speed, avoid falling into local optimal solution, the basic ant colony optimization (ACO) algorithm is improved to propose an efficient and intelligent ant colony optimization (IMVPACO)algorithm. In the IMVPACO algorithm, the updating rules and adaptive adjustment strategy of pheromones are modify in order to better reflect the quality of the solution based on the increment of pheromone. The dynamic evaporation factor strategy is used to achieve the better balance between the solving efficiency and solving quality, and effectively avoid falling into local optimum for quickening the convergence speed. The movement rules of the ants are modify to make it adaptable for large-scale problem solving, optimize the path and improve search efficiency. A boundary symmetric mutation strategy is used to obtain the symmetric mutation for iteration results, which not only strengthens the mutation efficiency, but also improves the mutation quality. Finally, the proposed IMVPACO algorithm is applied in solving the traveling salesman problem. The simulation experiments show that the proposed IMVPACO algorithm can obtain very good results in finding optimal solution. And It takes on better global search ability and convergence performance than other traditional methods. Keywords: