Parameter Estimation Using an Adaptive Immune Clone Selection Algorithm

A novel Artificial Immune Algorithm, namely Adaptive Immune Clone Selection Algorithm is proposed in this paper for parameter estimation which can be formulated as a multi-modal optimization problem with high dimension. In this method the secondary response, adaptive mutation regulation and vaccination operator are introduced in the generic Clone Selection Algorithm to improve the convergence speed and global optimum searching ability. Simulation results for identifying the parameters of a dynamic system are presented to demonstrate the effectiveness of the proposed method.