Artificial Immune Algorithm Optimization And Its Key Problems Research

Traditional artificial immune algorithm is not very well studied the internal mechanism of the immune clonal selection process, resulting in the stability of the algorithm by the antibody concentration.Meanwhile, the algorithm randomly generated populations will easily lead to the numerical values of non-uniform distribution of the solution space, thereby increasing the data redundancy phenomenon and may appear premature convergence phenomenon and the lack of crossover operation.In this paper, a summary and analysis of relevant research based on three key issues of the traditional artificial immune algorithm fully integrated feedback evolution depth model, the equivalence interval partitioning strategy and related optimization algorithm to conduct research. The main contents and innovations of this dissertation are summarized as follows:First, elaborate research results and methods about the basic concepts, basic operator,specific framework, as well as the traditional artificial immune algorithm model and typical application, which can provide reference for researchers on the fundmental theories of artificial immune algorithms. Finally, compare the advantages and disadvantages of artificial immune algorithms and other intelligent algorithms.Second, mutual stimulation and inhibition of the relationship between the antibody is based on the concentration of antibody in biological immune system, higher antibody concentration, the more suppressed; lower antibody concentration, the more promoted.According to instability of the algorithm for the level of antibody concentration, propose an evolutionary feedback depth model so as to effectively enhance the stability of artificial immune algorithm.Third, many optimization algorithms randomly generated populations in artificial immune system, will easily lead to numerical values of non-uniform distribution of the solution space, thereby increasing phenomenon of data redundancy, and design a equivalent interval division model used to solve the problem of data redundancy.Fourth, on the basis of the evolutionary feedback depth model, fully consider the two factors of the antibody concentration and diversity of the population, presents a clonal feedback optimization algorithm, described in detail design ideas and framework of the algorithm, and specific analysis of the stability of algorithms. The proposed algorithm integrates into an evolutionary feedback depth model and population survivability degrees design concept, effectively improve the stability of the algorithm. Finally, the proposed algorithm is applied to an independent task scheduling in grid computing, achieved better experimental results, which show that the method is feasible and effective.Fifth, this paper propose a chaos clonal optimize algorithm for function optimization based on the equivalence division model,described in detail the design ideas and the framework of the proposed algorithm,and analysis of its convergence using Markov chain theory. Meanwhile, the computational complexity of the algorithm carried out a detailed analysis. The proposed algorithm uses the chaos of randomness, ergodicity and regularity to avoid falling into local minimum, while introducing the equivalent divided strategies to reduce data redundancy phenomenon. The simulation experiments show that the proposed algorithm can be completed given the scope of search at a faster speed and global optimization task.Sixth, according to traditional clonal selection algorithm may exist premature convergence phenomenon and the lack of crossover operator problems, and propose a new efficient clonal annealing optimize algorithm, described in detail the design ideas and the framework of the algorithm, and analysis of its convergence using Markov chain theory. The proposed algorithm combines simulated annealing algorithm with clonal selection mechanism of immune system, and maintain the balance of global and local search. The proposed algorithm can effectively improve search efficiency, so as to speed up the convergence rate. Finally, the proposed algorithm is applied to the association rule mining, and has got high recall and precision.