随着当今世界逐渐从信息化转型为数据化，模式识别和数据挖掘等领域面临越来越大的挑战.爆炸式增大的数据量使得特征选择过程成为大数据模式识别等领域必不可少的环节.受动物界资源争夺行为启发，在由特征选择模型转变为资源分配问题模型中加入个体的资源争夺行为，提出多群体公平算法(multi-colony fairness algorithm, MCFA)对该行为进行评判和处理，用以取得更优的分配方案(即更优特征子集)，其有机融合随机搜索和启发式搜索，且将filter方法和wrapper方法相结合，降低计算量的同时获得更高的分类准确率.对提出的多群体公平算法进行了分析，从理论上证明了算法的收敛性和有效性；UCI机器学习数据库数据集与4种经典特征选择算法:顺序前向搜索算法(sequential forward selection, SFS)、顺序后向搜索算法(sequential backward selection, SBS)、顺序前向浮动搜索算法(sequential floating forward selection, SFFS)、顺序后向浮动搜索算法(sequential floating backward selection, SBFS)和3种主流特征选择算法：相关性-冗余度特征选择算法(relevance-redundancy feature selection, RRFS)、最大相关最小冗余算法(minimal-redundancy-maximal-relevance, mRMR)、ReliefF算法的对比实验表明，提出的多群体公平算法能够有效选择规模和性能都比较好的特征子集.

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。

旅行商问题（ＴｒａｖｅｌｉｎｇＳａｌｅｓｍａｎＰｒｏｂｌｅｍ，ＴＳＰ）是ＮＰ完全问题中最为著名的问题，它易于陈述而难于求解，至今尚未找到准确有效的求解大规模ＴＳＰ问题的方法．文中提出了能求出ＴＳＰ有效近似最优解的新的蚊子追踪（ＭｏｓｑｕｉｔｏＨｏｓｔＳｅｅｋｉｎｇ，ＭＨＳ）算法，证明了蚊子的目标追踪行为和ＭＨＳ数学模型的一致性、蚊子追踪算法的收敛性，并通过理论证明确定了ＭＨＳ算法中各参数的选择范围．蚊子追踪算法是一个全新的仿生算法．文中以ＴＳＰ问题为载体，详细提出了蚊子追踪算法的动机、生物学模型、数学模型、算法、理论基础（数学证明）及大量实验结果．从理论和实验两方面证明了蚊子追踪算法能够求出ＴＳＰ问题理论上的优化解

A wolf pack can hunt prey efficiently due to reasonable social team hierarchy and effective team cooperation. Inspired by the collective intelligence of wolf pack, in this paper, a novel swarm algorithm named the social team building optimization (STBO) algorithm is proposed for solving optimization problems. In order to mimic the method of social team building, which is an optimization process in reality, STBO algorithm is in terms of social team hierarchy, team building state and process control. Firstly, the social team model separates individuals of population into different swarms according to the appropriate team hierarchy. In this way, the proposed algorithm not only has fast search speed but also avoids to fall into the local optimum prematurely. Secondly, the team building state model divides the optimization process into three states. In different states, individuals at different levels act diverse social behaviors to make the algorithm maintain population diversity and possess better search capability. Thirdly, the team power model is designed to determine the states of optimization process by means of the team power and the team cohesion. The main aim of this model is to make the algorithm have a good balance between exploration and exploitation, namely to find the optimal solutions as possible as it can. Moreover, the mathematical models of STBO are educed by the swarm theory, the state evolution theory and the energy–entropy theory. Meanwhile, the convergence property of the presented algorithm has been analyzed theoretically in this paper. And STBO was compared to three classical nature-inspired algorithms on 11 basic standard benchmark functions and also three state-of-the-art evolutionary methods on CEC2016 competition on learning-based single-objective optimization. Some simulation results have shown the effectiveness and high performance of the proposed approach.

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.