协同人工免疫计算模型的研究
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摘要
针对当前人工免疫系统的通用模型在计算效能方面仍然存在的一些问题,本文通过借鉴生命科学中协同进化的一些概念和行为方式,如生态环境、物种影响等,探讨了一种协同人工免疫计算模型(CAIM)的实现方法,旨在改善当前该类计算模型中的不足。
该模型的核心思想是将一类物种的某种进化特性总结后作用于另一类物种相似的进化特性,以提高对应物种的进化过程。具体而言,首先,系统将每类物种的最优解集合进行曲线拟合,以得出该种群进化趋势参数表；其次,对两种群之间的相似度进行判断,如果相似度超过系统设定的阈值,则根据相似种群进化趋势参数表调整各自种群的参数使其种群得以快速进化；最后,系统动态调整种群规模以形成协同进化的生态格局。我们用双物种的聚类算法进行了简单的仿真实验,两物种在相似性上达到阈值之后,对其分别进行最优解集合的三次样条曲线拟合,根据相对应的参数调整以达到各自种群加速进化的效果。结果表明在该协同进化模型算法下,两物种所得到的最优解优于各自单独进化的结果,且寻优时间大大缩短。
在上述工作的基础之上,将此协同进化模型与人工免疫理论相结合,进一步针对协同人工免疫计算模型进行了深入的研究,使该模型应用于解决经典的TSP优化问题。其主要思想在于分析种群进化过程中,在免疫疫苗亦随之进化的情况下,如何建立疫苗库与种群间的协同进化机制以提高算法全局搜索最优解或者满意解的概率。通过理论分析和针对多组TSP问题的仿真计算,结果表明该模型在搜索最优解或满意解均优于传统的遗传算法,同时在寻优效率上有较大提升。
For the limited computing efficiency of existing artificial immune models, a novel method is proposed to improve their searching capabilities, which makes use of some coordinative mechanisms, such as ecological environment and species affected etc, with referring to such kind of models or concepts in natural world, discussion a means of computing model of Cooperation-based Artificial Immune Model (CAIM).
The core idea of the model is one species will be some sort of evolutionary characteristics of the role of sum of the other species in the evolution of similar characteristics, to enhance the evolution speed of corresponding species. Specifically, First, Species of each type of system will be the optimal solution set for curve fitting, come to the evolutionary trend of the population parameter table; Second, on the similarity between the two groups to judge, if the similarity of system settings for more than the threshold, then adjust the parameters of each species to the rapid evolution of its species; Finally, system dynamic adjust the size of species to form a co-evolutionary patterns of ecological. we use the two-species clustering algorithm to carry out a simple simulation experiment, At the after of similarity of the two species reached the threshold, separately for its optimal solution set of cubic spline curve fitting, according to adjust the corresponding parameters in order to achieve their respective species to evolve more rapidly to the effects of. The results proved that in the under of the model of co-evolutionary algorithm, the two species have been the optimal solution is superior to the results of their separate evolution, and optimization time significantly shortened.
Based on the above-mentioned, we were in combination with the co-evolution model and artificial immune theory, for in-depth study of CAIM, so that the model is applied to solve the classic TSP optimization problem. With regarded to the fact that a vaccine itself evolves along with population's evolution, this method aims at setting up a coordinative relation between the population and the vaccination-base during the whole evolutionary process, in order to raise the probability with which the algorithm finds the optimum or a satisfied solution. Both theoretical analysis and the simulation on multi examples of TSP problem, show that this model appears batter than traditional genetic algorithms, and the searching efficiency for the globally optimum is greatly improved as well.
该模型的核心思想是将一类物种的某种进化特性总结后作用于另一类物种相似的进化特性,以提高对应物种的进化过程。具体而言,首先,系统将每类物种的最优解集合进行曲线拟合,以得出该种群进化趋势参数表；其次,对两种群之间的相似度进行判断,如果相似度超过系统设定的阈值,则根据相似种群进化趋势参数表调整各自种群的参数使其种群得以快速进化；最后,系统动态调整种群规模以形成协同进化的生态格局。我们用双物种的聚类算法进行了简单的仿真实验,两物种在相似性上达到阈值之后,对其分别进行最优解集合的三次样条曲线拟合,根据相对应的参数调整以达到各自种群加速进化的效果。结果表明在该协同进化模型算法下,两物种所得到的最优解优于各自单独进化的结果,且寻优时间大大缩短。
在上述工作的基础之上,将此协同进化模型与人工免疫理论相结合,进一步针对协同人工免疫计算模型进行了深入的研究,使该模型应用于解决经典的TSP优化问题。其主要思想在于分析种群进化过程中,在免疫疫苗亦随之进化的情况下,如何建立疫苗库与种群间的协同进化机制以提高算法全局搜索最优解或者满意解的概率。通过理论分析和针对多组TSP问题的仿真计算,结果表明该模型在搜索最优解或满意解均优于传统的遗传算法,同时在寻优效率上有较大提升。
For the limited computing efficiency of existing artificial immune models, a novel method is proposed to improve their searching capabilities, which makes use of some coordinative mechanisms, such as ecological environment and species affected etc, with referring to such kind of models or concepts in natural world, discussion a means of computing model of Cooperation-based Artificial Immune Model (CAIM).
The core idea of the model is one species will be some sort of evolutionary characteristics of the role of sum of the other species in the evolution of similar characteristics, to enhance the evolution speed of corresponding species. Specifically, First, Species of each type of system will be the optimal solution set for curve fitting, come to the evolutionary trend of the population parameter table; Second, on the similarity between the two groups to judge, if the similarity of system settings for more than the threshold, then adjust the parameters of each species to the rapid evolution of its species; Finally, system dynamic adjust the size of species to form a co-evolutionary patterns of ecological. we use the two-species clustering algorithm to carry out a simple simulation experiment, At the after of similarity of the two species reached the threshold, separately for its optimal solution set of cubic spline curve fitting, according to adjust the corresponding parameters in order to achieve their respective species to evolve more rapidly to the effects of. The results proved that in the under of the model of co-evolutionary algorithm, the two species have been the optimal solution is superior to the results of their separate evolution, and optimization time significantly shortened.
Based on the above-mentioned, we were in combination with the co-evolution model and artificial immune theory, for in-depth study of CAIM, so that the model is applied to solve the classic TSP optimization problem. With regarded to the fact that a vaccine itself evolves along with population's evolution, this method aims at setting up a coordinative relation between the population and the vaccination-base during the whole evolutionary process, in order to raise the probability with which the algorithm finds the optimum or a satisfied solution. Both theoretical analysis and the simulation on multi examples of TSP problem, show that this model appears batter than traditional genetic algorithms, and the searching efficiency for the globally optimum is greatly improved as well.
引文
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