机器人学是现在及未来科技发展的重点，路径规划是机器人学中的一个重要课题.生物界一些群居动物有严格的等级制度和职责分工，受社会群居动物行为启发，提出社会群体搜索算法(social group search algorithm， SGSO).社会群体搜索算法对群体的分类及信息反馈机制——领导-追随机制的制定，降低了早熟的概率，交叉变异和淘汰机制的引入增加了搜索范围，减少了陷入局部最优的可能.同时，对提出的社会群体搜索算法进行了分析，从理论上证明了算法的收敛性；将社会群体搜索算法应用于机器人路径规划进行仿真，从实验中验证了算法的有效性，并与遗传算法和粒子群算法比较，进一步证明了社会群体搜索算法在机器人路径规划问题中的有效性和高效性.

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.

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 sociometric 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.

冬季湖面冰冻是一种常见的自然现象.受这一自然现象启发，提出了一种新的智能并行算法——湖水能量优化算法，并应用该算法解决旅行商问题.湖水能量优化算法模拟湖水降温时湖面的冰冻过程.随着温度的降低，湖水分子失去能量，当能量达到冰冻阈值时，分子析出结冰.湖水能量受到湖水中心能量、大气能量、湖水分子能量以及湖面风吹动等多方面影响.由此建立湖水能量优化算法的数学模型——湖水能量模型和风动模型等，并通过收敛性定理和Lyapunov稳定性定理进行理论证明，验证了算法的收敛性和解决旅行商问题的有效性.最后，通过实验模拟湖水能量优化算法解决TSPLIB中标准实例问题，并将实验结果与其他经典算法进行比较，进一步说明了湖水能量优化算法解决复杂NP难题时高效率、低迭代次数及强收敛性的特性.

The problem of bandwidth allocation in computer networks can be likened to the supply–demand problem in economics. This paper presents the economic generalized particle model(EGPM) approach to intelligent allocation of network bandwidth. EGPM is a significant extension and further development of the generalized particle model (GPM)[1]. The approach comprises two major components: (1) dynamic allocation of network bandwidth based on GPM; and (2) dynamic modulation of price and demands of network bandwidth. The resulting algorithm can be easily implemented in a distributed fashion. Pricing being the network control mechanism in EGPM is carried out by a tatonnement process. We dis-cuss the EGPM’s convergence and show that the approach is efficient in achieving the global Pareto optimum. Via simulations, we test the approach, analyze its parameters and compare it with GPM and a genetic-algorithm-based solution.