A kind of adaptive Immune Genetic Algorithm based on Chaos and its application

In order to overcome the shortcomings of Immune Genetic Algorithm´s relatively slow convergence rate during the process of solving large-scale optimization problem, given Chaos optimization´s benefits of sensitive to the initial value, easy to jump out of local minimum point, the fast search speed, global asymptotic convergence and so on, basing on the both search advantages of Immune Evolutionary Algorithm and Chaos Optimization Algorithm in their own space, considering that chaotic sequence could be simulated the proliferation of immune cells approach, we combined their characteristics of Chaos Optimization Algorithm and Immune Genetic Algorithm, making use of “exchange” and “shift” operations to solution matrix and memory matrix during the optimization process of chaos, and doing adaptive adjustment to selection probability and mutation probability during the Genetic operation, a kind of adaptive Immune Genetic Algorithm based on Chaos is proposed. The validity of the new algorithm is verified by real application.