New approaches of particle swarm optimisation algorithm based on Gaussian and Cauchy distributions to adjust the control points of B-spline neural networks are proposed. B-spline networks are trained by gradient-based methods, which may fall into local minimum during the learning procedure. To overcome the problems encountered by the conventional learning methods, particle swarm optimisation  ...

In this paper, particle swarm optimization, which is a recently developed evolutionary algorithm, is used to optimize machining parameters in hard turning processes where multiple conflicting objectives are present. The relationships between machining parameters and the performance measures of interest are obtained by using experimental data and swarm intelligent neural network systems (SINNS)....

Self-organization of a biologically motivated swarm into smaller subgroups of different velocities is found by solving a 1-dimensional adaptive-velocity swarm, in which the velocity of an agent is averaged over a finite local radius of influence. Using a mean field model in phase space, we find a dependence of this group-division phenomenon on the typical scales of the initial swarm in the posi...

In this work, a novel approach of merging two swarm intelligence algorithms is considered – one mimicking the behaviour of ants foraging (Stochastic Diffusion Search [5]) and the other algorithm simulating the behaviour of birds flocking (Particle Swarm Optimisation [17]). This hybrid algorithm is assisted by a mechanism inspired from the behaviour of skeletal muscles activated by motor neurons...

In this paper, we discuss the concentration dependency of pheromone communication in swarm robotics. Instead of a pheromone trail and the insect antenna, we used ethanol and an alcohol sensor. This experimental system has a trade-off problem; high concentrations of the pheromone yield high signal strength but the signal duration is short, while low pheromone concentrations yield low signal stre...

Rule abstraction is an intuitive new tool that we propose for implementing and controlling swarm systems. The methods presented in this paper encourage a new paradigm for designing swarm applications: engineers can interact with a swarm at the abstract (swarm) level instead of the individual (agent) level. This is made possible by modeling and learning how particular abstract properties arise f...

This paper investigates the relation between chaos and swarm intelligence. The swarm intelligent model is represented as an iterated function system (IFS). The dynamic trajectory of the particle is sensitive on the parameter values of IFS. The Lyapunov exponent and the correlation dimension were calculated and analyzed. Our preliminary research results illustrate that the performance of the swa...

Swarm robotics is a branch of collective robotics systems that offers a set of remarkable advantages over other systems. The global behavior of swarm systems emerges from the local rules implemented at the individual level. Therefore, characterizing a global performance obtained at the swarm level is one of the main challenges, especially under complex dynamics such as spatial interferences. In...

In this paper we described the hardware architecture of an inexpensive, heterogeneous, mobile robot swarm, designed and developed at RISC lab, University of Bridgeport. Each UB robot swarm is equipped with sensors, actuators, control and communication units, power supply, and interconnection mechanism. This article also describes the essential features and design of a dynamic power distribution...

This paper analyzes the collective behaviors of swarm robots that play role in the aggregation scenario. Honeybee aggregation is an inspired behavior of young honeybees which tend to aggregate around an optimal zone. This aggregation is implemented based on variation of parameters values. In the second phase, two modifications on original honeybee aggregation namely dynamic velocity and compara...