Immune Algorithm Based on Evaluation Mechanism

In order to optimize the complex functions, an Immune Algorithm Based on Evaluation Mechanism (IABEM) is proposed. Firstly, the algorithm remembers useful mutation information based on immune memory and then guide the clone operation and mutation operation by the judge and evaluation of the information, which can strengthen the local search ability. Secondly, to strengthen the global search ability, global information of contemporary population is considered into the new generation of individuals. Finally, the optimal individual runs self-learning in order to improve the precision of the algorithm. Simulation results on benchmark functions show that the algorithm is well suitable for the complex function optimization, and has the characteristics of rapider convergence, more powerful global search capability and high precision.