Immune Evolutionary Algorithms And Their Applications In Cooprative Multi-Robot System

Artificial Immune System (AIS) is a new intelligent method simulating natural immune system. It is a kind of computing system to solving many kinds of complex problems based on the functionalities, disciplines, characteristics and other related immune theories of biological immune system. The purpose of the AIS research is to extract the special information processing mechanisms contained in biological immune system, to build the corresponding models and algorithms, and to implement novel intelligent information processing systems.In this paper, some novel immune evolutionary algorithms based on the immune clonal selection are presented, including the affinity guided clone selection algorithm based on particle swarm optimization, the diversity guided immunity algorithm with mutation coevolution, and the multi population immunity coevolution algorithm. The applications of these algorithms to some numerical optimization tasks validate their potential of solving complex problem. Mean while, the immune algorithms are applied into cooperative path planning of multi mobile robots, the methods and experiment results of this kind of application are presented. And architecture of multi-robots system with evolutionary decision and coevolutional machnism is designed. The main work can be summarized as follows:1. In order to overcome the low convergence speed of ordinary immune algorithms, a noval immune algorithm with modified particle swarm evolutional equation is presented by analysis of theory and experiment, and its convergence is proved.2. The impact on the diversity of population of the mutation is discussed in great detail, and a diversity guided immune algorithm with mutation coevolution is presented. The theory analysis and simulation experiments prove that the algorithm improve the diversity of the population, and the convergence speed as well. The convergence of the algorithm is also proved. 3. Under the guidance of the general framework of coevolution, the framework of immune coevolution is established, and a multi population immune coevolution algorithm with sharing memory is presented. The character of this algorithm is recording the successful cooperative action in a sharing memory, so the coevolutional populations can exchange information in it and get a quicker convergence speed.4. Under the framework of evolutionary computation, immune path planning algorithms of robots are constructed, which integrate the vaccination with heuristic local search, clonal selection with parallel global search and immune network action control. The simulational experiments and expetiments of robots show that the method is effective.In a word, by studying the immune clone evolutionary algorithm, there are several modified immune evolutionary algorithms presented in this paper, architecture with evolutional capability of multi robots is designed, and the immune algorithms are applied into robots path planning. Theory analysis, simulational experiments and robots experiments all show that these algorithms and methods are effective.