A Mutation-Classified, Parameter-Dynamic Immunological Algorithm for Global Optimization

Based on the artificial immune system, a new clonal selection algorithm is proposed to perform global optimization. The concept of classified mutation is defined and the dynamic adjustment methods of some evolution parameters are introduced. The proposed algorithm is applied to several benchmark problems, and its performance is compared with other approaches in the literature. The results indicate that the new algorithm is a significant advance in clonal selection and a viable alternative.