Particle Swarm Optimization algorithm (Particle Swarm Optimization, PSO) in 1995, by the scholars of the Kennedy and Eberhart proposed a heuristic algorithm macro.
There are a number of relevant documents to prove the continuity of PSO in the optimization problem, quite a good search capability. In the discrete issues, such as scheduling, assigning, etc., there are many documents to PSO to solve this problem.
Among them, the travel salesman problem (Traveling Salesman Problem, TSP) is a typical optimization problem, many experts and scholars have been confirmed to be an NP-Complete problem. This study a wide range of components such as printed circuit boards routing, logistics, route planning, traveling salesman can use the concept to solve the problem.
In this paper, particle swarm optimization algorithm, through the mechanism of the conversion space (Transfer Space, TS) to deal with the issue of real-coded. Particles in order to avoid premature convergence and the optimal solution in the region, so the use of simulated annealing (Simulated Annealing, SA) for the regional search, the ability to allow particles beyond the optimal solution of the restricted region, so this paper a new method known as the XXX algorithm-XX-XX.
And try to fuzzy clustering (Fuzzy C-mean Clustering, FCM) algorithm for solving large traveling salesman problem of clustering methods. After experimental testing and comparison of the relevant literature showed that the method proposed in this article, in the absence of clustering of cases, 50 days to solve the urban problems of solving a relatively better capacity, and through the cluster approach for solving large traveling salesman problem , help to reduce the error rate of solving, computation time, complexity.