一种采用克隆选择的免疫差分进化算法

为了解决基本差分进化算法易出现早熟收敛的问题, 提出了一种融合人工免疫系统和差分进化的混合算法。该算法在差分进化过程中引入了克隆选择操作和受体编辑机制, 以增强算法的局部搜索能力和种群多样性。通过对五个标准函数的仿真实验表明, 该算法不仅可有效避免早熟收敛, 而且全局优化能力和收敛速度有显著提高。

Immune differential evolution algorithm using clone selection

To solve the problem of standard differerntial evolution algorithm getting into premature convergence easily, this paper proposed a novel hybrid optimization algorithm base on artificial immune system(AIS) and differential evolution(DE) algorithm. In this algorithm, it inserted clonal selection and receptor edit mechanism into the differential evolution process to enhance the local search ability and the population diversity of the algorithm. Experimental results on five functions show that this algorithm not only effectively avoids the premature convergence, but also significantly improves the global optimization ability and the convergence speed.