自适应免疫克隆选择文化算法

 免疫克隆选择算法中，单纯采用克隆选择机制的全局收敛能力较差，而采用（μ+λ）选择机制则容易陷入早熟收敛。为兼顾算法的搜索和探索能力，提出一类自适应免疫克隆选择文化算法。该算法采用文化算法的双层进化机制，提取并利用进化过程中的隐含知识，有机结合克隆选择和（μ+λ）选择两种机制，从而给出一种基于知识的自适应调整选择机制的混合选择策略。针对标准测试函数的仿真结果表明，该算法具有更稳定的全局收敛性能及较快的收敛速度。

Adaptive Immune Clonal Selection Cultural Algorithm

In immune clonal selection algorithms, global convergence ability is worse if clonal selection is only adopted. However, immune algorithm with (μ+λ) selection is easy to fall into premature convergence. In order to ensure the exploitation and exploration, an adaptive immune clonal selection cultural algorithm is proposed. Dual structure of cultural algorithm is adopted in the algorithm. And a hybrid selection strategy integrating (μ+λ) selection and clonal selection is put forward. The proportion of population influenced by each selection method is adaptively adjusted according to implicit knowledge extracted from the evolution process. Aiming at benchmark functions, simulation results indicate that the algorithms can effectively improve the speed of convergence and have better computation stability.