An improved adaptive immune genetic algorithm based on information entropy

摘要

The promotion on search efficacy of immune genetic algorithm is an enduring issue. An adaptive immune genetic algorithm based on information entropy is proposed. The parameters of similarity and affinity are designed based on information entropy. It integrated density control, improved crossover and mutation. The algorithm can make better use of global and local information and differences between antibodies for diversity control. The adjustments improve the speed, accuracy and convergence stability. Simulation results on multimodal function show that the proposed method has better optimization capability.