Ant Colony Algorithm Theory And Application Research

Ant Colony Algorithm is a novel heuristic algorithm to solve complicated optimization problems. It gets inspiration from nature behavior of ants in finding food. Because of its properties of robustness, global optimization, universality and distributed computation, the theoretical research is involved increasingly deep and the application becomes increasingly large. The theory part of ACO focuses on establishing the computation model of ACO, analyzing the convergence of ACO, etc. The application part of ACO generally deals with two aspects: to solve a kind of combinational optimization problem and to solve some specific real industry management problem. Compared with the theory aspect of ACO, the application aspect of ACO extends to rather wide fields and makes great success.This thesis studies the ACO algorithms theory and introduces two aspects of its applications. One is to a typical combination problem, Traveling Salesman Problem. The other aspect is an exercise in application field, Problem for Logistic Distribution. The main contributions of this thesis are:We devise IHAS, an improved hybrid algorithm combines max-min ant system (MMAS) with a local search strategy. We present the computation model of ACO and design several effective strategies. IHAS also uses adaptive pheromone trail information, neighborhood candidate list and Metropolis rulers to instruct solutions, which can speed up convergence, avoid local optimization and solve scalable instances.Introduce the IHAS to classical combinational problem TSP (Traveling Salesman Problem). TSP is a typical kind of NP hard problem to testify the efficiency of the improved algorithm. It had wide application background in transportation, network and so on. It is also the research foundation for logistic routing problem since there are some commons between TSP and the shortest path problem. Theory analysis and experimental result shows that IHAS outperforms other hybrid ant colony algorithms due to its ability to find the satisfactory results effectively when applied to the Traveling Salesman Problem.We put the improved algorithm into logistic transportation management. Design the computation model of logistic distribution routing problem and present IHAS to solve this problem. Satisfied computational result on given problems are reported, which shows that the improved ACS is useful and effective, the best path usually can be found.