改进免疫算法在函数优化中的应用

Improved immune algorithm and its application in function optimization

为了提高和加强免疫算法的搜索能力,本文在已有克隆选择算法(CSA)分析的基础上,提出利用生物的进化过程中子代抗体比父代抗体离最优解的启发性信息更近的原理,这种新的偏心动态免疫克隆算法简称EDICA。这种算法首先制定偏心变异的策略,并对其进行分析,目的是加快靠近最优解的速度和效率,以此提高免疫算法的精度。本文的实验结果显示出EDICA具有准确找到静态函数的能力和高精度锁定跟踪动态函数的优势。

In order to improve and strengthen the searching ability of immune algorithm, based on the existing clonal selection algorithm （ CSA ） based on the analysis, proposes the use ofbio logical evolution of neutrongener ation antibody than the principle of parent ant ihody closer to the optimal solution of heuristic information, the eccentric dynamic immune c]onal al gorithm of this new EDICA. This algorithm first develop edeceent ricmutation strategy, and carries on the analysis, the purpose is to speed up thenear optimal solution speed and efficiency, so as to improve the accuracy of immuneal gorithm.The experimental result sshow that EDICA has the ability of accurate finds tatic function and high precision track dynamic function advantage.