Improvement And Application Of Ant Colony Optimization

A population-based simulated evolutionary algorithm called ant colony optimization was proposed in 1992 by Italian researchers M.Dorigo,V.Maniezzo and A.Colorni. Many scholars are attracted to study ACO and in the past ten years the algorithm has been widely applied to the fields of combinatorial optimization,network routing,functional optimization,data mining and path planning of robot ctc, and good effects of application are gained.The dissertation focuses on the modification and applications of ACO,especially an in deep study on how to improve the basic ACO algorithm,hybridizing other algorithms and application in the field of combinatorial optimization.The main contributions fo this dissertation are as follows:First,an immune clonal-ant colony algorithm is presented in accordance with poor pheromone on the path early and stagnation.First, it adopts immune clonal algorithm to give information pheromone to distribute.Second,it makes use of the ant colony algorithm introducing immune clonal operator to get several solutions.This method is able to restrain stagnation during the iteration process effectively,and enhance the capability of search. For large dimension problems,it adopts the technology of enhancing the contrast to restrain blind of roulette-wheel.The speed of convergence is enhanced apparently.The improved algorithm are applied to Traveling Salesman Problem(TSP).The simulation results show that the proposed algorithm performs significantly better than the original algorithms.Secondly,inspiring from Ant Colony Algorithm (ACA) and Antibody Immune Clonal Algorithm (AICA), a new hybrid algorithm is presented to tackle 0-1 knapsack problem. We take full advantages of the ability of searching and the diversity which they provide respectively. Some parameters of the algorithm and the comparison with other algorithms have been performed. The experimental results show that the proposed algorithm is a perfect hybrid algorithm with higher performance.Finally, the work of this dissertation is summarized and the prospective of future research is discussed.