Improved Genetic Algorithm For TSP

GA is an optimization algorithm which is the formation of simulating the natural environment of the genetic and biological evolution. GA has the advantages of global search capability, implicit parallelism, robustness and simple operation, etc. It is often used to solve the problems which are difficult to solve by some of the traditional methods. Since its independent of the constraints which are restrictive assumptions in the search space, in particular, the search process is guided by the evaluation of the fitness function, which does not need the special field knowledge, GA's range of application is an extremely broad and it has been used practically in many fields.Traveling Salesman Problem (TSP) is a typical combinatorial optimization problem, because of the NP-completeness, it has been used as the classical instances that measure the merits of the algorithm, and it is favored by the majority of researchers.Purpose of this paper is to explore an effective way to solve the classical TSP in the way of solving the TSP based on the improved the genetic algorithm. This paper proposed two improvement strategies which are based on fitness proportional selection. Experimental results show, the proposed two improved methods in this paper can effectively improve the accuracy of the solution and have a better convergence.