Research And Application Of Improved Ant Colony Algorithm

Ant colony algorithm is a kind of swarm intelligence optimization algorithm. It is a new intelligent evolutionary algorithm which is a similar to the process of ant communities in search of food in nature. And it is an ideal method for solving difficult discrete problems. It fully demonstrated its advantages in many applications and obtained good results in terms of improved algorithms.Ant colony algorithm has the advantage of positive feedback, self-organization, distributed, robust, easy to combine with other algorithms. But often trapped in local optimal solution, convergence is slow, the initial solution is relatively high. Theoretically, It will more quickly resolve any combinatorial optimization problems, if the ant colony algorithm to make the appropriate changes. This article has been improved on hybrid ant colony algorithm and simulated annealing algorithm combines. It takes into account the objective function gradient of this factor so that global convergence to getting better. In addition, It also made related improvements in the angle optimization. For example, it takes into account the influence of the angle between the direction of the algorithm and the results have been very good.This paper put forward algorithm which is simulated annealing and ant colony hybrid algorithm based on the gradient of objective function and ant colony algorithm in the angle optimization.The numerical analysis and experiment show that the improved new algorithm not only possesses the advantages of the original algorithm, but also improve the running speed of the algorithm. Applied to the problem of TSP and path planning, The superiority of the new algorithm is verified.The paper contains following tasks:1. This paper briefly introduces the background and significance of research status of ant colony algorithm, and it also describes the content and significance of the study.2. Briefly introduces the basic principles of ant colony algorithm flow algorithm, it also introduces the advantages and disadvantages of the algorithm and so on.3. First introduces the basic principle and algorithm flow of simulated annealing algorithm, Then introduces the basic principles and the algorithm flow of simulated annealing and ant colony hybrid algorithm based on the gradient of objective function, Finally, we gave the experimental results on the new algorithm for solving problems of the TSP.4. First, a brief introduction path planning problem, Then introduces the basic principles and the algorithm flow of ant colony algorithm in the angle optimization, Finally, we gave the experimental results on the new algorithm for solving problems of the path planning.