An Improved Micro Artificial Immune Algorithm Utilizing Employed Honey Bees for the Identification of Nonlinear Systems


Abstract – This paper presents an improved micro artificial immune (IMAI) algorithm utilizing basic concepts from swarm intelligence. In particular, to enhance the searching capability of the recently developed micro artificial immune system (Micro-AIS) algorithm, employed honey bees are recruited to provide high-quality antibodies for the working population of the IMAI algorithm. The proposed algorithm is used to find the optimal kernel values for the Volterra series model to identify nonlinear systems. To demonstrate the efficiency of the proposed method, three different types of nonlinear systems are considered, including a highly nonlinear rational system, a heat exchanger, and a continuous stirred tank reactor (CSTR). For all these systems, the IMAI algorithm has achieved accurate modelling results and fast convergence rates. Moreover, a comparative study was conducted with other optimization methods, namely the original Micro-AIS algorithm, the improved particle swarm optimization (IPSO), the real-coded genetic algorithm (GA), the least mean squares (LMS), the least mean p-norm (LMP), and the least mean absolute deviation (LMAD). From this comparative study, the proposed IMAI algorithm has achieved the best modelling performance compared to the other methods.