Research And Application Of Ant Colony Algorithm

Ant colony algorithm is a kind of random search algorithm which is put forward by simulating the behavior of the real ant colony, which is applied to solve combinatorial optimization problems by heuristic search algorithm, such as genetic algorithm, particle swarm optimization, simulated annealing algorithm and taboo algorithm. Along With the expansion of algorithm application, it has been paid more and more attentions from scholars. At the same time, the ant colony algorithm can quickly and reliably solve the difficult problems with the characteristics of distributed computing, positive feedback mechanism and greed search algorithm. Therefore, in the field of bionic intelligence, ant colony algorithm is one of the research hot spot.Although the ant colony algorithm has been widely used in solving combinatorial optimization problems and static problem and so on, but it needs further in-depth in theory research.What’s more,it needs longer search time and is easily trapped in local minima in the operation process, is also susceptible to be interfered by the initial factors. Therefore, we need to further study ant colony algorithm. This article firstly summary the research status of the ant colony algorithm at home and abroad in recent years; Secondly this paper introduces the principle of ant colony algorithm, its mathematical model based on the TSP problem, the analysis of search strategies and using MATLAB software to analyze all parameters of the ant colony algorithm, abtaining the influence degree of the parameters on the algorithm performance; At the same time, some typical application examples of ant colony algorithm is introduced.Finally, an ant colony algorithm which integrates a genetic operator is proposed on the basis of the further study of the various improved ant colony algorithm model. In order to illustrate the contribution of the length between two nodes to the later ants to choose the path, this algorithm uses a new updating rule in the process of local pheromone updating; meanly, sequence operators of genetic algorithm is integrated into ant colony algorithm to reverse of solution and with Metropolis criterion to accept optimization solution, thus it can improve the solution acquired; Using orthogonal experiment design method in parameter Settings determines the optimal combination of parameters. The simulation results show that the proposed algorithm improves the convergence rate of the optimal solution.