Studies On Artificial Immune Algorithms And Applications

Artificial Immune System (AIS) is a computing system to solve the complex problemsbased on the functionalities, disciplines, characteristics and other related immune theories ofbiological immune system. The purpose of the AIS research is to extract the specialinformation processing mechanisms contained in biological immune system, to build thecorresponding models and algorithms, and to implement novel intelligent informationprocessing systems. AIS is a novel intelligent computing system developed with ArtificialNeural Network (ANN) and Evolutionary Computation (EC); and it's an interdisciplinaryresearch field derived from life science and computer science; and can be used to solve manykinds of technical problems relating to the national economy and society.Biological Immune System (BIS) is a self-adaptive, self-learning, self organization,parallel and distributed complex system. It's composed of many kinds of organs, molecules,lymphocytes and other cells with immune functionalities. The primary functionality of theBIS is to discriminate non-self from self, eliminate the non-self which is harmful to the bodyand maintain the balance of the body. BIS has many useful characteristics such as immunerecognition, immune memory, immune regulation, immune tolerance, immune surveillanceand so on.Artificial Immune Algorithm is an artificial optimization search algorithm constructedby the biological immunology and the mechanism of gene evolution. It is a mathematicalsimulation model to the biological immune behavior; and a very important algorithm ofimmune computation. In this paper, some artificial immune algorithms are introduced asfollows: Basic Immune Algorithm, Negative Selection Algorithm, Clonal SelectionAlgorithm, Immune Programming, and Immune Genetic Algorithm.The main work of this paper is: (1) By reading a lot of literature, we summarize someexisting immune algorithm and their computational process, and give an analysis theadvantages and disadvantages of these immune algorithms and their applications. (2) Theimmune clonal selection algorithm is used to solve the function optimization proBlems. The results show that the high performance of immune clonal selection algorithm for functionoptimization problems. (3) The VC++ code is written to solve the TSP problem using theclonal selection algorithm. Five examples of TSP problem Uleysses16, Uleysses22, Eil51,Eil76, Gr96 have been used in this paper for numerical simulation. The solution obtainedusing the colone selection algorithm is very close to the optimal solutions, which indicates theclonal selection algorithm is very effective for solving TSP problem. The work in this paperis a good research foundation for further studying and researching of artificial immunealgorithm.