Studying Of The Approximate Algorithms For Traveling Salesman Problem

Traveling Salesman Problem (TSP) is a classic combinatorial optimizes issue and an NP-hard. But in practice, it is applied in abroad field. In the past, people have been struggling to seek the new method that has not only high quality but also fast convergence rate all the while.One of the important methods in mathematic processing is solving the old problem using updating standard. In the past twenty years, many technologies of bionic algorithms spring up quietly, and pullulate companied with the developing of computer science synchronously. So a kind of new energy was emitted to the study of TSP.Following the former progeny, the paper designs two kinds of new approximate algorithms at first, which grounded on the basic idea of genetic algorithms in bionic computer technologic field. One is the commix genetic algorithms which fuses the idea of nearest algorithm, and make a conclusion which is convergence, furthermore approaching its best of all solving with the probability 1, this character of convergence will bring an active impact to the studying of genetic algorithms; The other is the heuristic method by infiltrating the idea of genetic algorithms into the inserting method. Then, the thesis checked up these algorithms by some representative examples, and makes sure the excellent performance of the two algorithms. At last, this paper discusses the question of multi-object optimization combined with the TSP, and providing the corresponding algorithm. The idea of dynamic searching based on the system level is very creative! It also advances a brand-new mode for studying the multi-object optimization.