Research On Evolutionary Algorithm Of Traveling Salseman Problem

TSP (Traveling. Salesman Problem) is one of the most famous amongthe combination optimization problems. It has the typical characteristics ofa lot of combination optimization problems. Theoretically speaking, theenumeration not only can solve TSP, but also can get the. best answer. Butto all computers nowadays, it's hardly to obtain its best answer in suchhuge search space by using common enumeration. Therefore, all kinds ofalgorithms to solve TSP emerged because of demand. Among of them, evolutionary algorithm brings us new means to solve the TSP.Evolutionary algorithm (EA) is an intelligent algorithm that learnsfrom the evolutionary process in the nature. EA employs a codingtechnology and some genetic operations. Under the pressure of selection, which means "fits survive", the algorithm can produce an optimalsolution. Because EA is simple and it seldom needs any additionalinformation about the problem, EA becomes a general solver of challengeproblems.In the TSP, Ant Algorithm (ACO)and the Particle Swarm Optimization(PSO) have demonstrated the ability to solve problems effectively, and GT(Guo Tao algorithm) perform wonderfully. This paper analyzes and studiesthe exchange and accumulation mode about pheromone in the improvedACO, the algorithm will not stopped earlier, a better solution can alsolikely be got and speed of the convergence can be guaranteed. Theimproved PSO formula about the speed and location is analyzed andstudied, the algorithm is charactered by the combinatorial optimizationproblems and it performs higher efficiency in searching better solution.After substituting the annular-reverse for the linear-reverse, introducingthe mapping and optimization operator and increasing some controlstrategies in the original GT, the annular-reverse direction will be determined by the path length of the DNA fragments, whether therandomly selected city will be accepted depends on the length of theDNA fragment contained it, because the figure of the TSP solution isannular, the local optimization is added into it according to certainprobility.