Development of a New Optimization Algorithm Based on Artificial Immune System and Its Application

In resent years, research has taken an interest in design of approximation algorithms due to the requirement of these algorithms for solving many problems of science and engineering like system modeling, identification of plants, controller design, fault detection, computer security, prediction of data sets etc. The area of Artificial Immune System (AIS) is emerging as an active and attractive field involving models, techniques and applications of greater diversity. In this paper a new optimization algorithm based on AIS is developed. The proposed algorithm has been suitably applied to develop practical applications like design of a new model for efficient approximation of nonlinear functions and identification of nonlinear systems in noisy environments. Simulation study of few benchmark function approximation and system identification problems are carried out to show superior performance of the proposed model over the standard methods in terms of response matching, accuracy of identification and convergence speed achieved.