Abstract:

In order to improve the search ability of the immune algorithm, a periodically varying mutation operator is designed based on immune mechanism and periodical evolution of organism. If only the affinity is taken as an immune selection evaluation criterion, low affinity antibodies will be overly inhibited. An improved immune selection operator is proposed by introducing the antibody concentration to the affinity as the evaluating index. The convergence of the improved immune algorithm is analyzed based on the Markov chain. In order to test the effectiveness of the algorithm, it is applied to solve the function optimization problems. Simulation results show that the improved algorithm has a higher search speed and accuracy.