Hybrid Immune Algorithm And Its Application In Solving TSP Problem

Immune Algorithm (IA) is a newly constructed Evolution Algorithm (EA) by transplanting the concepts and theory of biological immune system into Genetic Algorithm (GA). IA is easy to construct, has global convergence. It has been widely applied in many areas such as optimization, computer security. By using random searching method, IA guarantees its global convergence, but at the same time, its ability of finding local optimal is affected, and the convergence speed of IA is not satisfied. Traditional optimization methods use much more information of the target problem, so their convergence speed is much better, and the ability of finding local optimal is better. IA supplies us a global point searching technique, and traditional optimization methods supply us a local surface searching technique. By combining these two types of methods, we get so called Hybrid Immune Algorithm (HIA). It has global surface searching ability. In this thesis, research on IA, especially HIA, is reviewed at first, and an HIA is constructed to solve TSP problem by combining greedy algorithm, semi-greedy algorithm, and amendment-circle algorithm with IA. Realizing this algorithm with Matlab, and solving randomly generated tsp examples by it, the result gained is quite satisfied compared with IA and other traditional optimization methods.