Study On Artificial Immune Algorithm And Its Applications

Biological immune system is a highly parallel adaptive information learning system, which can identify and remove the antigenic eyewinkers invading the body. This system can learn, remember and adjust adaptively to keep the stabilization inside the body. During recent years, people begin to realize the revelatory significance of the biological immune mechanism to intelligent algorithm. Artificial immune algorithm (AIA) is this kind of new algorithm which is inspired by the biological immune system. This kind of algorithm has been used in many fields, such as machinery study, unconventionality and malfunction diagnosis, simulation of the behavior of robots, control of robots, inbreak detection of networks and etc. It has been a new effective member of the family of intelligent algorithms. In this paper, some basic concepts, framework, functions and principles of the biological immune system are introduced. Then the research range, research status and basic theory of the artificial immune system are simply analyzed. Based on the analysis on the variable scale immune algorithm, changing the definition of affinity, principle of mutation and principle of antibody restraint, an improved variable scale immune algorithm is proposed. Then this algorithm is realized by programming and a project to solve the transportation problems by the improved variable scale immune algorithm is put forward. Here three kinds of transportation problems—the produce and sale imbalance transportation problem, the transportation problem restricted by time and the transportation problem whose variable has upper limit, are been solved, validating the efficiency of the algorithm. Searching speed of this algorithm and the influence to it when changing some parameters are discussed, which proved the improved variable scale immune algorithm is robust. Then comparing the results of these problems by the improved variable scale immune algorithm to those by other algorithms, the efficiency of the algorithm is validated once more. Finally, considering the deficiency of the clone selection algorithm, some improvements are made and then an improved clone selection algorithm is proposed. This algorithm is also been realized by programming and a project to solve the channel assignment problem in cellular networks by the improved clone selection algorithm is put forward. Here seven benchmark problems are been solved, validating the efficiency of the algorithm. Searching speed of this algorithm and the influence when changing some parameters are discussed, which proved the improved clone selection algorithm is robust. Then the comparison of the results by improved clone selection algorithm to those by other algorithm validated the efficiency of the algorithm once more.