Atmospheric quality assessment model based on immune algorithm optimization and its applications

Owing to the low search precision of the traditional immune clonal selection algorithm, an improved immune clonal selection algorithm is proposed in this paper, which introduces vaccination strategy and local Gaussian mutation operator. The roulette selection, binary digit gene bit selection and inoculation strategies are all used during the vaccine pick-up, selection, and inoculation. Thus the phenomena without crossover for the genes of the antibody in the traditional immune clonal selection algorithm could be overcome, and the rate of the choiceness antibodies is improved. The local Gaussian mutation operator is also introduced into the improved algorithm. The step of Gaussian mutation operator is applied by self-adaptively adjusting continuously to improve the performance of local search. Besides, expanding search space strategy is applied to avoid getting into the local extremum, so the whole search capability of the proposed algorithm is greatly improved. Furthermore, an atmospheric quality assessment model based on immune clonal selection algorithm is proposed and it is applied to the field of atmospheric quality assessment. The experimental results show that the proposed algorithm could improve the precision and efficiency effectively for the problems to be solved. The proposed assessment model has good practicability and application perspective.