Immune Intelligence Research In Optimization And Its Application In Digital Image

Biological immune system is a complicated self-adaptive system, which owns the ability of preventing outside pathogens invading human's body according to lots of mechanism. It owns the abilities like immune memory, antibody's self-recognizing ability and immune diversity. Meanwhile biological immune system shows lots of intelligent characteristics, like recognizing and replying different antigen. Combing with these characteristics, immune algorithm has the abilities of excellent diversity, robustness and invisible parallelism. With the study of biological immune system and development of computer immunology, immune optimization is playing more and more important role in the areas of efficient optimization and intelligent computing. It has been applied in many areas, like pattern reorganization, fault diagnosis and computer safety. However, comparing with other intelligent algorithms, immune algorithm also owns some disadvantage, like premature convergence and poor ability in local optimization. Therefore, the improved research of immune intelligence has become the main and the most welcome area in network, intelligence, control and computer.In this thesis, I mainly research in theory of immune optimization under the background of computer immunology, and the following three aspects are my researching content.Firstly, I analyze and research the mechanism of algorithm, and combine it with the theory of optimization issues and digital images.Secondly, I research the theory, code and characteristics of basic algorithms of Immune Algorithm, Genetic Algorithm and Particle Swarm Optimization. Combing with the six testing functions, I analyze three aspects in convergence time, convergence times and ability of reaching the optimal value. On this basis, I analyze Immune Algorithm based on Clone, Immune Algorithm Based on High and Low Position and Immune Algorithm Based on Particle Swarm Optimization. The main analysis of three improved algorithms is on the arithmetic operators that show significant roles in the improved algorithms. Also i compare the three algorithms with basis algorithms according to simulation experiments.Finally, on the analysis of these algorithms, i propose two algorithms. The first enhanced algorithm named Adaptive Immune Algorithm Based on Particle Swarm Optimization. I compare it with the three algorithms that are Immune Algorithm Based on Clone, Immune Algorithm Based on High and Low Position and Immune Algorithm Based on Particle Swarm Optimization. After the analysis of algorithm mechanism, i apply it into the image clustering. The second improved immune algorithm i propose is in the way of seeking the global optimization. After two times of seeking global optimal threshold, i can use this threshold to get a satisfied edge image.