Research And Application Of Adaptive Immune Evolutionary Algorithm

Artificial immune system is an intelligent system developed by drawing lessons from the principle and mechanism of biological immune system.Artificial immune algorithm is a new intelligent optimization algorithm based on artificial immune system,while immune cloning selection algorithm is the most important artificial immune algorithm.Because in the clone selection algorithm,only the mutation operation is used for local search,the mutation method has a great impact on the performance of the clone selection algorithm.The strategy of mutation setting and the combination of clonal selection algorithm and other swarm intelligence optimization algorithms are important research contents of clonal selection algorithm.On the basis of previous researches,this paper studies the adaptive setting of mutation operation in the clonal selection algorithm and the adaptive selection of multi-evolution strategy in the immune multi-objective evolutionary algorithm.The main contents are as follows:1.Briefly introduces the research process and recent situation of evolutionary algorithm and artificial immune algorithm;The basic principle,algorithm flow,parameter setting and convergence analysis of evolutionary algorithm and artificial immune algorithm are introduced.2.In order to give consideration to the global exploration ability and local development ability of the algorithm,t mutation was introduced into the artificial immune cloning selection algorithm,and a method to determine the degree of freedom(n)of t distribution based on evolutionary algebra was presented.On this basis,an immune clonal selection algorithm based on t mutation is proposed.The performance of the new algorithm is tested by the standard test function and compared with the immune clonal selection algorithm based on Gaussian variation and Cauchy variation.The results of numerical experiments show that the new algorithm can achieve smooth transition between Gaussian variation and Cauchy variation and obtain better overall optimization effect than the algorithm based on Gaussian variation and Cauchy variation.3.The advantage of using clone selection in the immune multi-objective evolutionary algorithm is that it can improve the convergence rate,while the disadvantage is that it can reduce the diversity of the population to some extent.Is proposed in this paper a kind of based on the variation of the objective function,the evolution strategy more adaptive immune multi-objective evolutionary algorithms,the basic idea is:the immune multi-objective evolutionary algorithm based on clonal selection as the foundation,according to the rate of change of target function,the different stages of the evolution of adaptively choosing two different differential evolution strategy,in the guarantee of algorithm convergence speed at the same time both the diversity of population,avoid the algorithm falls into local optimum.The performance of the new algorithm is tested by using the DTLZ test function,and compared with other algorithms,the results verify the convergence and effectiveness of the new algorithm.4.Atmospheric quality assessment is an important and complicated issue in environmental science.The present research shows that the intelligent optimization algorithm can solve this kind of problem well.In this paper,the immune clonal selection algorithm based on t mutation is used to simulate the evaluation of atmospheric quality.Figure[20]table[5]reference[60]...