Novel evolutionary strategy based on artificial immune response for constrained optimizations

Based on the clonal selection theory and mechanisms of biological immune response, a novel artificial immune systems model, Artificial Immune Response (AIR), is discussed. And based on Artificial Immune Response a novel evolutionary strategy for constrained optimizations is put forward. Analysis of its network framework shows that the new algorithm is convergent with a higher probability than (μ,λ) evolutionary strategy. The experiments on 10 benchmark problems show that when compared with the (μ,λ) evolutionary strategies adopting stochastic ranking method and dynamic penalty function method, the new evolutionary strategy is capable of improving the search performance significantly no matter in convergent speed or precision.