Immune Clonal Selection Optimization Method with Mixed Mutation Strategies

Abstract

In artificial immune optimization algorithm, the mutation of immune cells has been considered as the key operator that determines the algorithm performance. Traditional immune optimization algorithms have used a single mutation operator, typically a Gaussian. A mixed mutation strategy may be more efficient than a single one. In view of this, a mixed mutation strategy using Gaussian and Cauchy mutations is presented, and a novel clonal selection optimization method based on clonal selection principle is proposed also. The experimental results show the mixed strategy can obtain the same performance as the best of pure strategies or even better in some cases.