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.

The Load Rebalancing Problem (LRP) that reassigns tasks to processors so as to minimize the maximum load arises in the context of dynamic load balancing. Many applications such as on Web based environment, parallel computing on clusters can be stated as LRP. Solving LRP successfully would allow us to utilize resources better and achieve better performance. However LRP has been proven to be NP-hard, thus generating the exact solutions in tractable amount of time becomes infeasible when the problems become large. We present a new nature-inspired approximation algorithm based on the Waterflow Particle Mechanics (W-PM) model to compute in parallel approximate efficient solutions for LRPs. Just like other Nature-inspired Algorithms (NAs) drawing from observations of physical processes that occur in nature, the W-PM algorithm is inspired by kinematics and dynamics of waterflow. The W-PM algorithm maps the classical LRP to the flow of water flows in channels by corresponding mathematical model in which all water flows flow according to certain defined rules until reaching a stable state. By anti-mapping the stable state, the solution to LRP can be obtained.

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 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.

Creative thinking, which plays an essential role in the progress of human society, has an outstanding problem-solving ability. This paper presents a novel creativity-oriented optimization model (COOM) and algorithm (COOA) inspired by the creative thinking process. At first, COOM is constructed by simplifying the procedure of creative thinking while retaining its main characteristics. And then, COOA is presented for continuous optimization problems. It is a realization of COOM. As a new nature-inspired algorithm, COOA is different from other similar algorithms in terms of the basic principle, mathematical formalization and properties. Features of the COOM and the corresponding algorithm include a powerful processing ability for the complex problems, namely high-dimensional, highly nonlinear and random problems. The proposed approach also has the advantages in terms of the higher intelligence, effectiveness, parallelism and lower computation complexity. The properties of COOA, including convergence and parallelism, are discussed in detail. The numerous simulations on the CEC-2013 real-parameter optimization benchmark functions’ problems have shown the effectiveness and parallelism of the proposed approach.