An Improved Immune Clonal Selection Algorithm and its Applications

摘要

Owing to the problems of traditional immune clonal selection algorithm shortcoming such as slow search, low precision, always getting into local values etc. An improved immune clonal selection algorithm which is introduced into local Gaussian mutation operator is proposed in this paper. Moreover some other strategies, for example, expanding search space, aging, death and taboo algorithm and so on are also applied in the improved immune clonal selection algorithm in the paper. In addition, the proposed algorithm is used to optimize the parameters in the formula of S growth curve index, thus we can obtain an assessment model for atmospheric comprehensive pollution that is suitable to the cases of multi-pollutants, and then it was applied in the field of assessing atmosphere quality. Experimental results show that the proposed immune algorithm could overcome effectively premature convergence, improve the convergence speed while ensuring population diversity. The assessment method proposed for atmosphere quality has many advantages such as pellucid principle, physical explication and correct assessment results etc. It is a new effective approach for intelligence theory and technology applied in the field of atmosphere environment. Therefore it has great potential in the field of assessment of the atmospheric quality.