Complex Analysis of Anisotropic Swarms with Gaussian Profiles
This paper considers an M-member "individual-based" continuous time swarm model with individuals moving with a nutrient profile (or an attractant/repellent) in an n- dimensional space. It is proved that the swarm members aggregate and eventually form a cohesive cluster of finite size for Gaussian Profiles. Moreover, all the swarm members will converge to more favorable areas of the Gaussian profile under certain conditions.

title: Stability Analysis of Swarm with Interaction Time Delays Using Nearest Neighbors Information
abstract: This paper investigates the collective behavior of a leader-follower system with communication time lags based on nearest neighbors information. The leaders proposed in the swarm can obtain the information from the environment, but the followers can not. Under the common assumptions, we prove that the individuals of the swarm with the local information will aggregate and form a cohesive cluster of finite size. Moreover, we can clearly see the effects of the time delays on the dynamic of the swarm from the simulations.

title: Flocking of Multi-Vehicle Systems With A Leader
abstract: We study the coordinated motion of a group of vehicles with a leader based on nearest neighbor rules. The leader may be a special vehicle in the group or an external signal (virtual leader) to steer the vehicle group. The control law consists of two parts: a potential field force that makes the vehicles attract to each other and at the same time avoid collision in the group, and an alignment force that makes all vehicles' headings and speeds to converge respectively to common values. We assume that the interaction patterns are either fixed or switching based on the information topologies of the leader and member vehicles. Our approach is based on the Lyapunov theory and basic graph theory. We also consider the effect of noise on the collective dynamics of the group. Numerical simulations are worked out to illustrate the analytical results

title: Controllability of a Leader-Follower Dynamic Network with Interaction Time Delays
abstract: This paper studies the controllability of a leader-follower network of dynamic agents in the presence of communication delays. The network dynamics is governed by nearest neighbor rules over a fixed communication topology. The leader is a particular agent acting as an external input to steer the other member agents. We derive sufficient conditions of the controllability of the dynamic network and give an example to illustrate the main results.

title: Collective Behavior Analysis of a Class of Social Foraging Swarms
abstract: This paper considers an anisotropic swarm model that consists of a group of mobile autonomous agents with an attraction-repulsion function that can guarantee collision avoidance between agents and a Gaussian-type attractant/repellent nutrient profile. The swarm behavior is a result of a balance between inter-individual interplays as well as the interplays of the swarm individuals (agents) with their environment. It is proved that the members of a reciprocal swarm will aggregate and eventually form a cohesive cluster of finite size. It is shown that the swarm system is completely stable, that is, every solution converges to the equilibrium point set of the system. Moreover, it is also shown that all the swarm individuals will converge to more favorable areas of the Gaussian profile under certain conditions. The results of this paper provide further insight into the effect of the interaction pattern on self-organized motion for a Gaussian-type attractant/repellent nutrient profile in a swarm system.

title: Stability Analysis of Social Foraging Swarm with Interaction Time Delays
abstract: This paper considers a swarm model with an attraction-repulsion function involving variable communication time lags and an attractant/repellent. It is proved that for quadratic attractant/repellent profiles the members of the swarm with time delays will aggregate and form a cohesive cluster of finite size in a finite time. Moreover, all the swarm members will converge to more favorable areas of the quadratic attractant/repellent profiles under certain conditions in the presence of communication delays.

title: Analyzing the interestingness of association rules from the temporal dimension
abstract: Rule discovery is one of the central tasks of data mining. Existing research has produced many algorithms for the purpose. These algorithms, however, often generate too many rules. In the past few years, rule interestingness techniques were proposed to help the user find interesting rules. These techniques typically employ the dataset as a whole to mine rules, and then filter and/or rank the discovered rules in various ways. We argue that this is insufficient. These techniques are unable to answer a question that is of critical importance to the application of rules, i.e., can the rules be trusted? In practice, the users are always concerned with the question. They want to know whether the rules indeed represent some true and stable (or reliable) underlying relationships in the domain. If a rule is not stable, does it show any systematic pattern such as a trend? Before any rule can be used, these questions must be answered. The paper proposes a technique to use statistical methods to analyze rules from the temporal dimension to answer these questions. Experimental results show that the proposed technique is very effective.

title: An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers
abstract: In this paper, an effective hybrid algorithm based on particle swarm optimization (HPSO) is proposed for permutation flow shop scheduling problem (PFSSP) with the limited buffers between consecutive machines to minimize the maximum completion time (i.e., makespan). First, a novel encoding scheme based on random key representation is developed, which converts the continuous position values of particles in PSO to job permutations. Second, an efficient population initialization based on the famous Nawaz-Enscore-Ham (NEH) heuristic is proposed to generate an initial population with certain quality and diversity. Third, a local search strategy based on the generalization of the block elimination properties, named block-based local search, is probabilistically applied to some good particles. Moreover, simulated annealing (SA) with multi-neighborhood guided by an adaptive meta-Lamarckian learning strategy is designed to prevent the premature convergence and concentrate computing effort on promising solutions. Simulation results and comparisons demonstrate the effectiveness of the proposed HPSO. Furthermore, the effects of some parameters are discussed.

title: Entropy-based metrics in swarm clustering
abstract: Ant-based clustering methods have received significant attention as robust methods for clustering. Most ant-based algorithms use local density as a metric for determining the ants' propensities to pick up or deposit a data item; however, a number of authors in classical clustering methods have pointed out the advantages of entropy-based metrics for clustering. We introduced an entropy metric into an ant-based clustering algorithm and compared it with other closely related algorithms using local density. The results strongly support the value of entropy metrics, obtaining faster and more accurate results. Entropy governs the pickup and drop behaviors, while movement is guided by the density gradient. Entropy measures also require fewer training parameters than density-based clustering. The remaining parameters are subjected to robustness studies, and a detailed analysis is performed. 
 
In the second phase of the study, we further investigated Ramos and Abraham's ( Proc 2003   2003. pp 1370–1375) contention that ant-based methods are particularly suited to incremental clustering. Contrary to expectations, we did not find substantial differences between the efficiencies of incremental and nonincremental approaches to data clustering.

title: Constrained Nonlinear State Estimation - A Differential Evolution Based Moving Horizon Approach
abstract: A solution is proposed to estimate the states in the nonlinear discrete time system. Moving Horizon Estimation (MHE) is used to obtain the approximated states by minimizing a criterion that is the Euclidean form of the difference between the estimated outputs and the measured ones over a finite time horizon. The differential evolution (DE) algorithm is incorporated into the implementation of MHE in order to solve the optimization problem which is presented as a nonlinear programming problem due to the constraints. The effectiveness of the approach is illustrated in simulated systems that have appeared in the moving horizon estimation literature.

title: WBEM Based Distributed Network Monitoring
abstract: In the paper we identify the needs in efficient management of distributed networks and some problematic areas in the field. Then we introduce Web Based Enterprise Management (WBEM) to address the problem of providing a unified way to model all kinds of managed elements in a single information model in heterogeneous network environments. The advantages brought about by the use of WBEM in network management solve some critical problems existing in current network management. Based on the brief description of components of WBEM, we discuss in depth the basic WBEM instrumentation and multi-tiered WBEM enabled management infrastructure. We also apply this multi-tiered management infrastructure to a network management application scenario to monitor network activities for unexpected behaviors.