人工免疫系统的若干关键问题研究
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摘要
人工免疫系统是受自然免疫原理启发而建立的计算模型,由阴性选择算法、克隆选择算法与免疫网络三部分构成。阴性选择算法模拟T细胞精确的自体／异体识别能力,并成功应用于异常检测问题,但是该算法生成的检测系统存在漏洞且计算代价高,影响实际应用。克隆选择算法则模拟了自然免疫系统在应对外界攻击时的免疫响应过程,并将响应过程中表现出的免疫特征应用到高维全局优化问题,但在优化过程中,克隆选择算法表现出早熟收敛、全局搜索能力不足的问题。免疫网络模拟了自然免疫系统,但该模型中存在大量的参数、非常不成熟,导致实际应用价值不高。
本论文研究了阴性选择算法与克隆选择算法中存在的几个关键问题,研究成果及主要创新如下：
(1)为降低阴性选择算法的时间复杂度,提出了一种应用种子个体连续位刺激变异的检测器生成策略。首先随机生成种子检测器集合,根据其与自体的亲和度选定变异个体和变异片段；其次在被选个体的特定基因片段发生刺激-应答变异,产生新的候选检测器个体；最后应用r位连续匹配准则筛选候选个体生成新的检测器。该策略的特点在于利用种子个体和自体集合的模式信息指导变异过程,降低候选检测器与自体的匹配成功率。实验表明,在保持高检测率的同时,种子检测器变异算法比穷举算法、个体随机变异算法和检测器连续胞体超变异算法的生成效率更高。
(2)提出层次匹配算法用以降低阴性选择算法的时间复杂度。首先从理论上证明了层次匹配算法的有效性,其次依据r位连续匹配准则将自体集合分解为多个模式子集合以获得构成检测器的组件,最后通过二叉树接合组件得到检测器集合；层次匹配算法充分利用自体模式以提高搜索成功率、缩短生成时间；实验结果表明,在同样的实验环境下层次匹配算法比传统算法和位变异算法有更好的性能。
(3)针对阴性选择算法生成的异常检测系统存在大量漏洞,提出了一种能够探测系统全部漏洞的非检测模式漏洞探测算法(EHANDP)。首先指出了目前检测系统漏洞探测算法(EHASP)的不完备性；然后利用问题空间中的串模式证明了空间中个体成为漏洞的充分必要条件,并提出探测系统漏洞的完备性算法EHANDP;能够找出给定系统的全部漏洞是该算法的主要特点。实验中采用随机数据集和人工数据集比较了两种漏洞探测算法。实验结果表明,EHANDP算法不仅与EHASP有相同的计算复杂度,而且有更强的探测能力。
(4)为了克服免疫算法在优化高维多峰函数时存在的早熟收敛问题,提出一种高效的混合免疫进化算法。动态克隆扩张、基于存档机制的超变异和多母体交叉是该算法的主要特点。同时提出了一种算法性能评价准则,以比较不同算法在优化高维函数时的性能。在实验部分,首先使用经典测试函数测试了混合免疫进化算法的性能,然后分别在不同的评估次数下比较了自适应差分进化、基本免疫算法和混合免疫进化算法。实验结果表明免疫进化算法在求解精度、稳定性等方面均明显优于前两种算法。
(5)针对免疫算法在全局优化过程中多样性不足的问题,提出一种新型的免疫进化算法。提出的α-随机克隆扩张和多受体随机编辑算子是该算法的主要特色,同时引入改进的超变异算子以加强个体的学习能力。在实验环节中,首先通过优化经典的经典测试函数确定了进化群体大小和克隆扩张比；其次,根据算法性能评估准则,比较了免疫进化算法、快速克隆算法和Opt-IMMALG算法,结果表明免疫进化算法明显优于另外两种算法。
Artificial immune system, inspired by natural immune principle, is a new computational model, and it contains three computational models:negative selection algorithm, clonal selection algorithm, and immune network. Negative selection algorithm simulates T-cell's abilities of identifying self and non-self accurately, and is applied to generate abnormal detection systems. But systems exists holes so that some non-self individuals are not detected. On the other hand, the computational complexity of generating abnormal detection systems is high. The above aspects of negative selection algorithm affect its applications in real world. The second computational model is clonal selection algorithm. The algorithm simulates the process of immune response triggered when natural immune system is attacked by external entities. Clonal selection algorithm is employed to global high-dimensional optimization problems. However, the algorithm demonstrates premature convergence and insufficient of diversity information. The next computational model is immune network, which tries to simulate the whole natural immune network. The model which contains many control parameters and few attention is focused on it.
In the paper, some problems coming from negative selection algorithm and clonal selection algorithm are studied. Main contributions and innovations of the dissertation are shown as follows.
(1) A new detector generation strategy, based on seed individuals and contiguous somatic simulating mutation, was proposed to reduce the time complexity of negative selection algorithm (NSA). Firstly, the strategy produced seed detectors, and determined the special detectors and gene segment by measuring the affinity between seed set and self set; Then a stimulated-response mutation (SRM) occurred in a special gene fragment to obtain the newly candidate individuals; Finally the new competent detectors were selected according to r-contiguous bits matching rule. The characteristic of the algorithm is the pattern information used to guide the mutation process for reducing the matching rate of candidate individuals. The experimental results show that the algorithm outperforms several similar algorithms based on mutation operator in term of time complexity and coverage.
(2)Hierarchy Match Strategy (HMS) is proposed to decrease the computational cost of negative selection algorithm. The foundation of HMS is proved in theoretical firstly. Then HMS constructs the components of detector set through dividing the self set into several pattern subsets according to r-contiguous bit match rule. Lastly Detector set is obtained by using binary tree combing components. Using the self pattern information in generating process is the novelty of the algorithm, and which is the difference between the conventional generation strategies. The experimental result shows that HMS improves the performance in term of both detective rate and time complexity under the same experimental environment.
(3) A novel exploring holes algorithm based on non-detector pattern (short for EHANDP) was proposed for holes existing in anomaly detection system generated by negative selection algorithm. Incompleteness of current exploring holes algorithm grounded on self pattern (short for EHASP) was point out. And then the sufficient and necessary condition for individuals to be holes was proven using the string patterns in problem space, what is more, an exploring holes algorithm named EHANDP was proposed. The capability of finding all holes of a given detection system is EHANDP's main feature. The above two algorithms are compared using random dataset and artificial dataset, and the results shows that EHANDP algorithm outperforms than EHASP in the term of exploring capability although they have the same computational complexity.
(4) In order to overcome the premature of immune algorithm when solving high dimensional multimodal functions, an efficient hybrid immune evolutionary algorithm is proposed. The main characteristics of the novel hybrid algorithm are dynamic clonal selection, archive-based hypermutation and multi-parentic crossing operators. In addition, a novel performance evaluation criterion for comparing different algorithms is constructed in the paper. In experimental study, firstly the performance of proposed HIEA is validated using several classical test functions; next HIEA is compared with self-adaptive differential evolution (SaDE) and simple immune algorithm (SIA) under certain amount of function evaluations, the experimental results show that the performance of proposed HIEA is significant better than that of SaDE and SIA in term of the accuracy and stability.
(5) In order to increase the diversity of immune algorithm when solving high dimensional global optimization problems, a novel immune evolutionary algorithm (IEA) is proposed. The main characteristics of IEA are clonal expansion and multiple-parent random receptor editor operators. In addition, a modified hypermutation operator is introduced to improve the learning ability of individuals. In the experimental study, firstly several typical test functions are used to determine the population size and the ratio of clonal expansion. Next, the IEA is compared with fast clonal algorithm (FCA) and Opt-IMMALG, and the experimental results of the IEA are significantly better than that of FCA and Opt-IMMALG in terms of the performance evaluation criterion proposed.
本论文研究了阴性选择算法与克隆选择算法中存在的几个关键问题,研究成果及主要创新如下：
(1)为降低阴性选择算法的时间复杂度,提出了一种应用种子个体连续位刺激变异的检测器生成策略。首先随机生成种子检测器集合,根据其与自体的亲和度选定变异个体和变异片段；其次在被选个体的特定基因片段发生刺激-应答变异,产生新的候选检测器个体；最后应用r位连续匹配准则筛选候选个体生成新的检测器。该策略的特点在于利用种子个体和自体集合的模式信息指导变异过程,降低候选检测器与自体的匹配成功率。实验表明,在保持高检测率的同时,种子检测器变异算法比穷举算法、个体随机变异算法和检测器连续胞体超变异算法的生成效率更高。
(2)提出层次匹配算法用以降低阴性选择算法的时间复杂度。首先从理论上证明了层次匹配算法的有效性,其次依据r位连续匹配准则将自体集合分解为多个模式子集合以获得构成检测器的组件,最后通过二叉树接合组件得到检测器集合；层次匹配算法充分利用自体模式以提高搜索成功率、缩短生成时间；实验结果表明,在同样的实验环境下层次匹配算法比传统算法和位变异算法有更好的性能。
(3)针对阴性选择算法生成的异常检测系统存在大量漏洞,提出了一种能够探测系统全部漏洞的非检测模式漏洞探测算法(EHANDP)。首先指出了目前检测系统漏洞探测算法(EHASP)的不完备性；然后利用问题空间中的串模式证明了空间中个体成为漏洞的充分必要条件,并提出探测系统漏洞的完备性算法EHANDP;能够找出给定系统的全部漏洞是该算法的主要特点。实验中采用随机数据集和人工数据集比较了两种漏洞探测算法。实验结果表明,EHANDP算法不仅与EHASP有相同的计算复杂度,而且有更强的探测能力。
(4)为了克服免疫算法在优化高维多峰函数时存在的早熟收敛问题,提出一种高效的混合免疫进化算法。动态克隆扩张、基于存档机制的超变异和多母体交叉是该算法的主要特点。同时提出了一种算法性能评价准则,以比较不同算法在优化高维函数时的性能。在实验部分,首先使用经典测试函数测试了混合免疫进化算法的性能,然后分别在不同的评估次数下比较了自适应差分进化、基本免疫算法和混合免疫进化算法。实验结果表明免疫进化算法在求解精度、稳定性等方面均明显优于前两种算法。
(5)针对免疫算法在全局优化过程中多样性不足的问题,提出一种新型的免疫进化算法。提出的α-随机克隆扩张和多受体随机编辑算子是该算法的主要特色,同时引入改进的超变异算子以加强个体的学习能力。在实验环节中,首先通过优化经典的经典测试函数确定了进化群体大小和克隆扩张比；其次,根据算法性能评估准则,比较了免疫进化算法、快速克隆算法和Opt-IMMALG算法,结果表明免疫进化算法明显优于另外两种算法。
Artificial immune system, inspired by natural immune principle, is a new computational model, and it contains three computational models:negative selection algorithm, clonal selection algorithm, and immune network. Negative selection algorithm simulates T-cell's abilities of identifying self and non-self accurately, and is applied to generate abnormal detection systems. But systems exists holes so that some non-self individuals are not detected. On the other hand, the computational complexity of generating abnormal detection systems is high. The above aspects of negative selection algorithm affect its applications in real world. The second computational model is clonal selection algorithm. The algorithm simulates the process of immune response triggered when natural immune system is attacked by external entities. Clonal selection algorithm is employed to global high-dimensional optimization problems. However, the algorithm demonstrates premature convergence and insufficient of diversity information. The next computational model is immune network, which tries to simulate the whole natural immune network. The model which contains many control parameters and few attention is focused on it.
In the paper, some problems coming from negative selection algorithm and clonal selection algorithm are studied. Main contributions and innovations of the dissertation are shown as follows.
(1) A new detector generation strategy, based on seed individuals and contiguous somatic simulating mutation, was proposed to reduce the time complexity of negative selection algorithm (NSA). Firstly, the strategy produced seed detectors, and determined the special detectors and gene segment by measuring the affinity between seed set and self set; Then a stimulated-response mutation (SRM) occurred in a special gene fragment to obtain the newly candidate individuals; Finally the new competent detectors were selected according to r-contiguous bits matching rule. The characteristic of the algorithm is the pattern information used to guide the mutation process for reducing the matching rate of candidate individuals. The experimental results show that the algorithm outperforms several similar algorithms based on mutation operator in term of time complexity and coverage.
(2)Hierarchy Match Strategy (HMS) is proposed to decrease the computational cost of negative selection algorithm. The foundation of HMS is proved in theoretical firstly. Then HMS constructs the components of detector set through dividing the self set into several pattern subsets according to r-contiguous bit match rule. Lastly Detector set is obtained by using binary tree combing components. Using the self pattern information in generating process is the novelty of the algorithm, and which is the difference between the conventional generation strategies. The experimental result shows that HMS improves the performance in term of both detective rate and time complexity under the same experimental environment.
(3) A novel exploring holes algorithm based on non-detector pattern (short for EHANDP) was proposed for holes existing in anomaly detection system generated by negative selection algorithm. Incompleteness of current exploring holes algorithm grounded on self pattern (short for EHASP) was point out. And then the sufficient and necessary condition for individuals to be holes was proven using the string patterns in problem space, what is more, an exploring holes algorithm named EHANDP was proposed. The capability of finding all holes of a given detection system is EHANDP's main feature. The above two algorithms are compared using random dataset and artificial dataset, and the results shows that EHANDP algorithm outperforms than EHASP in the term of exploring capability although they have the same computational complexity.
(4) In order to overcome the premature of immune algorithm when solving high dimensional multimodal functions, an efficient hybrid immune evolutionary algorithm is proposed. The main characteristics of the novel hybrid algorithm are dynamic clonal selection, archive-based hypermutation and multi-parentic crossing operators. In addition, a novel performance evaluation criterion for comparing different algorithms is constructed in the paper. In experimental study, firstly the performance of proposed HIEA is validated using several classical test functions; next HIEA is compared with self-adaptive differential evolution (SaDE) and simple immune algorithm (SIA) under certain amount of function evaluations, the experimental results show that the performance of proposed HIEA is significant better than that of SaDE and SIA in term of the accuracy and stability.
(5) In order to increase the diversity of immune algorithm when solving high dimensional global optimization problems, a novel immune evolutionary algorithm (IEA) is proposed. The main characteristics of IEA are clonal expansion and multiple-parent random receptor editor operators. In addition, a modified hypermutation operator is introduced to improve the learning ability of individuals. In the experimental study, firstly several typical test functions are used to determine the population size and the ratio of clonal expansion. Next, the IEA is compared with fast clonal algorithm (FCA) and Opt-IMMALG, and the experimental results of the IEA are significantly better than that of FCA and Opt-IMMALG in terms of the performance evaluation criterion proposed.
引文
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