Stability Analysis of Stochastic Swarm Systems

In this paper, we present a model of stochastic swarm system and prove the stability of this kind of systems. We establish the stable aggregating behavior for the group using a coordination control scheme. This individual-based control scheme is a combination of attractive and repulsive interactions among the individuals in the group, which ensures the cohesion of the group and collision avoidance among the individuals. The dynamics of each individual depends on the relative positions between the individuals and the influences of the random disturbances. Under the influences of the noises, this position-based control strategy still generates the stable aggregating behavior harmoniously for the group and the self-organized swarm pattern is formed.

Steady-State Configuration and Tension Calculations of Marine Cables Under Complex Currents via Separated Particle Swarm Optimization?

Under complex currents, the motion governing equations of marine cables are complex and nonlinear, and the calculations of cable configuration and tension become difficult compared with those under the uniform or simple currents. To obtain the numerical results, the usual Newton-Raphson iteration is often adopted, but its stability depends on the initial guessed solution to the governing equations. To improve the stability of numerical calculation, this paper proposed separated the particle swarm optimization, in which the variables are separated into several groups, and the dimension of search space is reduced to facilitate the particle swarm optimization. Via the separated particle swarm optimization, these governing nonlinear equations can be solved successfully with any initial solution, and the process of numerical calculation is very stable. For the calculations of cable configuration and tension of marine cables under complex currents, the proposed separated swarm particle optimization is more effective than the other particle swarm optimizations.

Artificial moment method for swarm robot formation control

The purpose of this paper is to develop a general control method for swarm robot formation control. Firstly, an attraction-segment leader-follower formation graph is presented for formation representations. The model of swarm robot systems is described. According to the results and two kinds of artificial moments defined as leader-attraction moment and follower-attraction moment, a novel artificial moment method is proposed for swarm robot formation control. The principle of the method is introduced and the motion controller of robots is designed. Finally, the stability of the formation control system is proved. The simulations show that both the formation representation graph and the formation control method are valid and feasible.

Parameters identification of chaotic systems based on artificial bee colony algorithm combined with cuckoo search strategy

Artificial bee colony(ABC) algorithm is motivated by the intelligent behavior of honey bees when seeking a high quality food source. It has a relatively simple structure but good global optimization ability. In order to balance its global search and local search abilities further, some improvements for the standard ABC algorithm are made in this study. Firstly, the local search mechanism of cuckoo search optimization(CS) is introduced into the onlooker bee phase to enhance its dedicated search; secondly, the scout bee phase is also modified by the chaotic search mechanism. The improved ABC algorithm is used to identify the parameters of chaotic systems, the identified results from the present algorithm are compared with those from other algorithms. Numerical simulations, including Lorenz system and a hyper chaotic system, illustrate the present algorithm is a powerful tool for parameter estimation with high accuracy and low deviations. It is not sensitive to artificial measurement noise even using limited input data.