The host-seeking behavior of mosquitoes is very interesting. This paper is motivated by the following general observation on mosquito groups and their host-seeking behavior in nature: (1) Mosquitoes’ behavior has possession of the parallelism, openness, local interactivity and self-organization. (2) Mosquito groups seek host very fast. (3) The host-seeking behavior is similar to the producerscrounger process, which assumes that group members search either for “finding” (producer) or for “joining” (scrounger) opportunities. It stimulates us to extend a mosquito system model in nature to group mosquito hostseeking model (GMHSM) and algorithm (GMHSA) for intelligent computing. In this paper, we propose GMHS approach and show how to use it. By GMHSM, the TSP is transformed into the kinematics and dynamics of mosquito groups host-seeking process. The properties of GMHSM and GMHSA, including the correctness, convergence and stability, have been discussed in this paper. The GMHS approach has many advantages in terms of multiple objective optimization, large-scale distributed parallel optimization, effectiveness of problem-solving and suitability for complex environment. Via simulations, we test the GMHS approach and compare it with other state-of-art algorithms.

The development of the Internet of Things brings new opportunities and challenges for sensor networks.The scale of sensor networks tends to be larger. And the fusion rules need to be intelligent. In this paper, we propose a new Internet of Things group search optimizer (ITGSO) to solve intelligent information fusion problems in the high-dimensional multi-sensor networks. ITGSO is inspired by the latest research achievement about leader decision in Nature and works about social coordination, which mainly consists of three parts: basic group search optimizer, binary group search optimizer and social decision model. With ITGSO, we need less time to obtain minimum Bayes cost than particle swarm optimization. And information of uncertain social intelligent problems can be fused. In this paper, we give the theoretical basic of ITGSO and proved its validity via mathematical analysis and simulation results.

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.

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

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.