树突细胞算法的运行时间属性分析

第二代人工免疫系统中的树突细胞算法(DCA)是受先天性免疫系统中树突细胞(DCs)功能的启发而开发的算法，它已被成功运用于许多计算机安全相关领域。但是对DCA理论方面的分析工作很少，对算法大多数理论方面的研究也较少出现。而其它的人工免疫算法如负选择算法、克隆选择算法在理论方面的研究工作却出现在很多文献中。因此对DCA算法执行相似的理论分析，确定算法的运行时间变量、揭示其它算法属性就显得非常重要。论文给出了两个基于算法输入数据流的运行时间变量，并且证明了这两个变量是如何对算法输入数据与算法运行时变量进行关联，也揭示了在给定时间窗内基于输入数据的算法行为，而这些都与实际应用执行的算法无关。论文的研究工作为算法的进一步应用开发提供了指导。

Analysis on Runtime Essences of the Dendritic Cells Algorithm

Dendritic cell algorithm (DCA) belong to the second artificial immune system(AIS) is inspired by functions of the dendritic cells (DCs) of the innate immune system, and has been successfully applied to numerous security-related problems. However, theoretical analysis of the DCA has barely been performed, and most theoretical aspects of the algorithm have not yet been revealed. Other immune inspired algorithms, such as negative and clonal selection algorithms, were theoretically presented in many literatures. As a result, it is important to conduct a similar theoretical analysis of the DCA, to determine its runtime variable and other algorithmic properties, in line with other artificial immune algorithms. Two runtime variables of the algorithm are formulated based on the input data. It proved how these formulas relate the runtime variables of the algorithm to the input data, and how the algorithm behaves within a given time window based on the input data, without actually running the algorithm. These works in this paper can be used as guidelines for further application development of the algorithm.