Improvement and implementation of evolution immune algorithm in neural

In this paper, the algorithm combined the construction methods of immune strategies and immune operator, better solved the degradation phenomenon appeared in the algorithm, and the convergence speed has been improved significantly. Comparing with the differential evolution algorithm, adding differential evolution operator in the immune algorithm can increase antigen recognition, memory function and regulatory function. This algorithm not only failed to reduce the differential evolution algorithm robustness, but also took into account the global and local search capabilities; at the same time, the selection strategy based on antibody concentration made up the shortcomings of the algorithm to be easy fall into local excellent when the group diversity is bad, which improved the algorithm group diversity.