| With the gradual complication of the study object, traditional control theory base on precise model and optimized algorithm with exactness meet the great difficulty .people are enlightened by biology evolution and bionics, put forward a lot of heuristic intelligence optimizations. Ant Colony Optimization(ACO) is a kind of novel intelligence optimizer, which is applicable to the solution of many complicated combinatorial optimization problems especially. After being presented by Dorigo and others in the early 1990, It has been paid attention to people.and successfully applied to some fields. Parallel computation mechanism is adopted in this algorithm. ACO has strong robustness and is easy to combine with other methods in optimization, but it has the limitation of stagnation, and is easy to fall into local optimums. In this dissertation, improvement and application of Ant Colony Optimization are mainly discussed. The major innovations in this article are asfollows:A dynamic and adaptive ant colony algorithm is presented in accordance with the defect of early variety and stagnation. The contribution of the algorithm includes an adaptive strategy of pheromone, the limited range of pheromone, and a local updating for pheromone dynamically. This method is able to restrain stagnation during the iteration process effectively, and enhance the capability of search. The improved algorithm are applied to Traveling Salesman Problem (TSP). Experimental results for solving TSP of 14 dots, CTSP and Eil50 are proved to be effective.Present an improved ant colony algorithm to solve the location of distribution center problem. A dynamic local updating rule is presented in later cycle. Reduce the pheromone of the routes that are not selected by ants, increase the difference between the better routes and the worse routes, enhance the Convergent speed of the algorithm. Decrease the run time. Experimental results for a real example are... |
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