Research On Improved Ast Colony Algorithm And The TSP Simulaiton

Ant colony algorithm is a kind of novel intelligent optimization algorithm, with theadvantages of good robustness, positive feedback and distributed computing, so it isvery useful to solve complex combinatorial optimization problems. Now it is being paidattention and researched by more and more scholars. However, the algorithm still hassome defects, compared with other intelligent algorithms, its searching time generallylonger, and easily to fall into local optimal value.In this paper, aim at ant colony algorithm for solving TSP problems of slowconvergence and convergence accuracy is not high defect, this paper proposes twoimproved ant colony algorithms.(1)This paper proposes an adaptive ant colony optimization algorithm to solve TSP.The algorithm puts forward a new update pheromone mechanism and expand localsearch space strategy. The simulation results show that the improved algorithm inconvergence speed and convergence precision, has the very big enhancement comparedwithACS algorithm and general MMAS algorithm.(2) This paper proposes ant colony optimization algorithm based on bee colonysearch thought. The algorithm adopts a new mechanism on path selection andpheromone update. adopting the new path selection mechanism to expand the searchspace of the algorithm, effectively avoid stagnation phenomenon in the process ofsearch. Chaos perturbation introduced in pheromone update, help algorithm to find abetter solution near the current optimal value, improving the precision of solution. Thesimulation results show that the overall performance is greatly improved compared withACS algorithm and general MMAS algorithm.