Applied Research On Swarm Intelligence Optimization Algorithm

Many problems in science, engineering and economics need optimization. Aerial path, satellite resources, missiles track, time and order of aerial landing and takeoff, and other problems can be ended in TSP problem. In the thesis, TSP problem can be solved by Swarm Intelligence. The main achievements of this thesis include:Firstly, in target of solving the problem of Swarm Intelligence getting into local minimum easily and solution set unsteadiness, transcendental pheromone method is introduced. Its stabilization is fine and iterative times are also decreased.Secondly, ACO which is based on Elitist Strategy has been improved. This method employs rational pheromones, and decision-making method which can decrease subjective will. Thus, it can jump out of local minimum quickly.Thirdly, MAX-MIN ant system has been improved. Pheromone presents several dynamic arrangements. If the rational parameters are designed, local minimum can be jumped out easily.Finally, in order to improve the search capacity of global optimal value and local optimal value, one omnibus algorithm which includes GA,PSO,ACO and Greedy Method is presented.The thesis employs Oliver30 and C-TSP as simulation tests. The results of simulation are satisfied.