The Improved Genetic Algorithm Of Traveling Salesman Problem

Traveling Salesman Problem is a typical problem in the field of combination optimization, and it comes down to getting the minimal value of many variables. It is simple to state,but it is difficult to solute,and now it is considered as NP problem. But this problem exists and is used in a lot of fields, and it is the concentrated generality and simplified form of many complicated problem emerging from all kinds of field. Solving it fleetly and effectively has not only great theoretical function, but also important pragmatic value. Therefore, the purpose of this article is solving Traveling Salesman Problem through improved Genetic Algorithms.One of the obvious characters of modern science and technology's development is that life science and engineering science promoting, permeating and crossing each other. According to the characteristic and research status nowadays of Traveling Salesman Problem, this article chose Genetic Algorithms to solve it. In the first place, the article introduced the elements and basic implement technology of Genetic Algorithms, and expatiated the specialty of Genetic Algorithms expressly. Moreover, this article put up some idiographic improvement to traditional Genetic Algorithms.Traditional Genetic Algorithms adopt order city code and edge code. This article provide a new matrix code in response to their default. Its advantages are the following two: firstly, it's more stable than the order city code; secondly, it can show the answer more obvious than the edge code. Moreover the matrix form incline to tell whether gene satisfies legal and calculate its fitness. The three codes mentioned above will produce many illegal genes, so that according to the particular form of matrix code, we present a better way of reconstruction, that is matrix mutation. It can invert a great deal of illegal genes into legal ones so as to guarantee the variety of genes and extend space of solution, finally restrain the optimal value. Thus, the process of using improved Genetic Algorithms to solve Traveling Salesman Problem can be practices at Matlab 6.5. The result of imitation shows that improved Genetic Algorithms is better than the traditional one in the compute time.