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An Improved Immune Genetic Algorithm for Multi-peak Function Optimization

The biological immune system when attacked can always find the right antibodies to destroy the antigen and can maintain the diversity of antibodies. The combination of genetic and immunity in biology has been shown to be an effective approach to solving the degeneration of genetic algorithm in the late iterative optimization. According to the characteristic that the injected vaccine immune system can accomplish quickly identifying the antigen, the immune genetic algorithm has been improved in this paper. An improved immune genetic algorithm (IIGA) which has been improved in this paper is proposed Based on this theory for Benchmark function optimization. The results show that the IIGA can prevent the algorithm degenerative effectively during the process of optimization of the genetic algorithm, and improve the convergent speed of the algorithm.