计算机安全系统与生物免疫系统具有很多的相似性,它们都需要在不断变化的环境中维持自身的稳定性。提出复合免疫算法,并应用到入侵检测系统中,以保护网络安全。针对经典的人工免疫算法在性能上存在的缺陷进行了改进,完善了其核心算法——否定选择算法,在否定选择算法中加入了分段技术和关键位,避免了恒定的匹配概率导致的匹配漏洞,降低了系统漏检率。并将遗传算法中的克隆选择算法和改进的否定选择算法结合为复合免疫算法,提高了检测器生成的动态性和多样性。最后,通过数学理论分析与仿真实验模拟,验证了改进算法的有效性和可行性,并且与其它经典算法进行了比较,结果证明,改进算法可以提高系统性能。

Due to the dynamic structure in network topology and absence of a centralized administration in management, a specific routing algorithm satisfying the demands of QoS is required indeed in mobile Ad Hoc networks. A novel Social Group Search Optimizer algorithm is pro-posed by improving the GSO algorithm to a dynamic and discrete algorithm through the introducing of social behaviors. SGSO is divided into search and prey parts, where “search” is on duty to find the optimal solution effectively and “prey” is responsible for adjusting the algorithm to the dynamic change of objective functions. Dynamic Coupling Level is used to divide the Ad Hoc network and corresponding approaches and models based on SGSO are applied to routing algorithm, including the decision factor and local routing table. The convergence and correctness of our algorithm are verified mathematically and extensive experiments have been conducted to evaluate the efficiency and effectiveness of the proposed mechanism in mobile Ad Hoc networks. The results show that SGSO improves packet delivery ratio and reduces average end-to-end latency effectively, especially for large-scale and high-dynamicnetworks.

Social Networks (SN) is an increasingly popular topic in artificial intelligence research. One of the key directions is to model and study the behaviors of social agents. In this paper, we propose a new computational model which can serve as a powerful tool for the analysis of SN. Specifically, we add to the traditional socio metric methods a novel analytical method in order to deal with social behaviors more effectively, and then present a new bio-inspired model, the coordination generalized molecule model (CGMM). The proposed analytical method for social behaviors and CGMM are combined to give an algorithm that can be used to solve complex problems in SN. Traditionally, SN models were mainly descriptive and were built at a very coarse level, typically with only a few global parameters, and turned out to be not sufficiently useful for analyzing social behaviors. In this work, we explore bio-inspired analytical models for analyzing social behaviors of intelligent agents. Our objective is to propose an effective and practical method to model intelligent systems and their behaviors in an open and complex unpredictable world.

In this paper, a clustering algorithm based on emotional preference and migratory behavior (EPMC) is proposed for data clustering. The algorithm consists of four models: the migration model, the emotional preference model, the social group model and the inertial learning model. First, the migration model calculates the probability of individuals being learned, so that individuals can learn from the superior. Second, the emotional preference model is introduced to help individuals find the most suitable neighbor for learning. Third, the social group model divides the whole population into different groups and enhances the mutual cooperation between individuals under different conditions. Finally, the inertial learning model balances the exploration and exploitation during the optimization, so that the algorithm can avoid falling into the local optimal solution. In addition, the convergence of EPMC algorithm is verified by theoretical analysis, and the algorithm is compared with four clustering algorithms. Experimental results validate the effectiveness of EPMC algorithm.

随着人工智能和大数据的迅猛发展，大数据的爆炸式增长和问题的复杂性分布导致对并行智能处理的要求日趋迫切.传统的理论模型和技术方法面临严峻挑战，受自然界启发的物理学法则和生物学方法逐渐成为研究热点.受多头绒泡菌的生长觅食等行为启发，提出了一种基于能量机制的多头绒泡菌动力学算法(physarum-energy dynamic optimization algorithm, PEO).该算法以多头绒泡菌算法为基础，根据其动力学特征，引入能量机制，以改进现有的多头绒泡菌算法全局信息交互能力差等缺点.此外，PEO引入了年龄因子的概念和扰动机制，以控制算法在不同阶段的寻优能力和收敛速度，并从理论角度对算法模型的收敛性进行证明.最后，通过在TSP数据集上实验证明算法在不同规模数据集的有效性和收敛性，并进行了参数分析.与其他的优化算法的对比实验数据表明，PEO在面对复杂问题的求解速度和收敛速度明显优于其他的优化算法，具有高精度和快收敛的特性.