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.

Nature has always been a muse for those who dream in art or science. As it goes, optimization algorithms inspired by nature have been widely used to solve various scientific and engineering problems because of their intelligence and simplicity. As a novel nature-inspired algorithm, the crystal energy optimizer (CEO) is proposed in this article. The proposed CEO is motivated by the following general observation on lake freezing in nature: the dynamics of crystals have possession of parallelism, openness, local interactivity, and self-organization. It stimulates us to extend a crystal dynamic model in physics to a generalized crystal energy optimizer for traveling salesman problems, so as to exploit the advantages of crystal dynamic system and to realize the aforementioned purposes. The proposed CEO has these advantages: (1) it has the ability to perform large-scale distributed parallel optimization; (2) it can converge and avoid local optimum; and (3) it is flexible and easy to adapt to a wide range of optimization problems

Abstract—The collective intelligent behaviors of insects or animal groups in nature have maintained the survival of the species for thousands of years. In this paper, a novel swarm intelligence algorithm called the social group entropy optimization (SGEO) algorithm is proposed for solving optimization tasks. The proposed algorithm is based on the social group model, the status optimization model, and the entropy model, which are the main contributions of this paper. First, the social group model and the feedback mechanism between Leaders and Followers are developed to reduce the probability of local optimum. Second, the status optimization model is described to reveal the changing rule about the population behavior states, to support the conversion between different social behaviors during evolution, to promote the algorithm to optimize quickly, and to avoid local optimization. Third, the entropy model is introduced to analyze the entropy of social groups, the change rule of difference entropy, and to set the information entropy as behavior’s criterion of state optimization. In addition, the mathematical model of the SGEO is deduced from the group theory, matter dynamics, and the information entropy theory. The convergence and parallelism of it have been analyzed and verified theoretically. Moreover, to test the effectiveness of the SGEO, it is used to solve benchmark functions’problems that are commonly considered within the literature of evolutionary algorithms. Experimental results are compared with those of three other state-of-the-art algorithms. The superior performance of the SGEO validates its effectiveness and efficiency for the optimization problems, especially for the high-dimension problems.

撒谎行为的存在会破坏CDN缓存分配的公平性。 使用博弈论对服务器在缓存分配过程中的自私撒谎行为进行了研究。经分析发现,服务器撒谎行为的本质就是当缓存不足时,额外多申请一定量缓存；而当缓存充足时,则诚实地申请所需缓存量。针对这种撒谎行为,提出了一种公平分配算法,在计算服务器的缓存申请量时,考虑其历史缓存申请量,并根据不同阶段申请量的有效性不同引入年龄因子,(重新)计算得到服务器的当前有效缓存申请量,使得撒谎的服务器与诚实的服务器相比受到更多损失,以此来促使其停止撒谎行为。同时,公平算法还保证了系统的最大吞吐量,并引入了价格机制来保证诚实的服务器得到更高的需求满足度。仿真实验结果表明,公平算法对于上述撒谎行为有很好的改善效果。

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.