Mixed Ant Colony Algorithm And Their Applications

Ant colony optimization algorithm, called ant colony algorithm, is a probabilistic algorithm for finding the optimal path, which used of biological information as the basis for the ants chose the follow-up actions. Each ant will according to the intensity of pheromone at the intersection and makes the selection on the probability, which to observe and influence their future behavior. The algorithm is inspired by the foraging behavior of real ants in the wild, introduced by Dorigo M and in the early 1990s. With the extraordinary merits of distributional concurrency, stronger robustness and easily combining with other algorithms, this algorithm attracts more and more domestic and foreign scholar's attentions, and becomes the research focus and front question of heuristic algorithm at present in domestic and foreign.In the theory, the part of the ant colony algorithm theory are studied and three effective strategies are improved in this dissertation, such as, a new initial pheromone matrix, cluster is used in local search and mixed algorithm, to improve the ant colony algorithm. In the application, typical combinational optimization problems, TSP and traffic routing problem are selected in this dissertation, for solving them with improved ant colony algorithm. Simulation results show that the effectiveness and the feasibility of improved algorithm.The main content of this dissertation is as follows:(1) At the foundation of reading lots of domestic and foreign literature material, the article summarizes currently research productions and problems which would urgently be solved. The representative NP problem---traveling salesman problem is studied, the development background, principles, implementation and performance of ant colony optimization are introduced in detail, the algorithm itself is studied deep, and some improved ant colony optimizations are proposed and simulated in this dissertation.(2) Aimed at the shortcomings, which needing much time and easier to fall in local optimal solution in the ant colony algorithm, an improved algorithm is proposed. Through employing the list of candidate cities in the initial pheromone matrix to decrease inferior solutions and using cluster to do the second search in the local search, the searching range of algorithm can be narrowed, the quality of the solution space can be improved and the searching speed can also be raised. The simulations result for TSP problem shows that the algorithm is improved greatly in convergence rate and ability of global optimization.(3) Aimed at the actual traffic in the multi-objective problem, a multi-objective optimization method based on hierarchical GA-AS algorithm was proposed. The hierarchical structure was adopted in this algorithm by constraints. The ant colony algorithm was used in bottom, and genetic algorithm was used in upper. The former can be explored the optimal solution from the global space, but it can't do in the local. The latter from the local space to reach the optimal solution, and the algorithm can enhance the local searching ability. The simulation results show that this algorithm not only has a strong effect of practical applications, but substantially reduces the number of optimization calculations and improves the performance of the algorithm.