用于约束优化的人工免疫响应进化策略

A Novel Evolutionary Strategy Based on Artificial Immune Response for Constrained Optimizations

基于克隆选择学说及生物免疫响应过程的相关机理,探讨一种新的人工免疫系统模型———人工免疫响应,提出用于解决约束优化问题的人工免疫响应进化策略;基于算法网络拓扑结构的分析表明,新算法比传统的进化策略(μ,λ)-ES具有更大的收敛概率.对10个标准测试问题的测试结果表明,与采用随机排序的进化策略和采用动态惩罚函数的进化策略相比,新算法在收敛速度和求解精度上均具有一定的优势.

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.