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中文摘要：
基于浮点数编码，提出一种分级变异的动态免疫克隆选择优化算法．根据抗体的亲和度将种群分解为3个子种群，分配以不同的搜索任务，实施不同的变异策略．在进化过程中动态改变种群规模、克隆规模和变异参数，从而加快了全局搜索速度，提高了局部搜索精度．对5个复杂函数的优化仿真实验表明了该算法的有效性。
英文摘要：
A dynamic immune clonal selection algorithm with classified mutation is proposed based on floating point coding. To speed up the global search and improve the local convergence precision, the following two main strategies are introduced. According to the antibody affinity in relation to the antigen, the antibody population is decomposed into several subsets, and they are submitted to respective mutation processes for their different given tasks. Then, the population size, the clone size and the mutation parameters are dynamically changed with evolution processing. The proposed algorithm is used to optimize 5 complex functions for testing and the results show its effectiveness.
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