免疫入侵检测理论中克隆选择是检测器进化的关键.传统克隆选择算法通过比较样本间的亲和力累加值筛选样本,该方法具有较低的时间复杂度,但也造成了检测器的高重叠,影响迭代效率.将检测器个体的筛选与进化转化为pareto最优解的求解过程,提出了多目标优化理论的检测器克隆选择算法.实验表明,检测器基数不变的情况下,该算法明显提升了每代种群在进化过程中的检测范围,精简了记忆检测器的数量,提高了检测阶段系统的检测率.%In immune invasion detection theory,clonal selection is the key todetectorevolution.The traditional clonal selection algorithm,which compares the cumulative value of affinitybetween samples to select samples,has lower time complexity,but also causes high overlap of detectors and affects the iterative efficiency.This paper transforms the selection and evolution of detector individuals into the solving process of pareto optimal solution,and proposes the detector clone selection algorithm based on multiobjective optimization theory.Experiments show that the algorithm can significantly improve the detection range of each population in the evolutionary process,reduce the number of memory detectors and improve the detection rate of the detection system.
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