A CGS-MSM PGA Based on Multi-agent and Its Application in Solving TSP

TSP (traveling salesman problem) is a typical combinational optimization problem. At present, there are a lot of methods to solve this problem, but with the increase of the scale of this problem, most methods face the difficult problem of "combination exploding". This paper combines multi-agent theory and CGS-MSM PGA (coarse grain size-master slaver model parallel genetic algorithm) together, propose one improvement CGS-MSM PGA based on multi-agent, then use this PGA to solve TSP. This PGA is made up of many agent, solves the TSP by the coordination between many agents inside the multi-agent system. The introduction of multi-agent theory, make the master course and slave course of CGS-MSM PGA to be made of agent, so the ability of communication and coordination raise greatly, thus can not only overcome the shortcoming of original CGS-MSM PGA, but also utilize its advantage at large, so can solve the problem "combination exploding" brought by the scale of TSP increases.