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...

This paper proposes a TSK-type Neuro-Fuzzy system tuned with a novel learning algorithm. The proposed algorithm used an improved version of the standard Particle Swarm Optimization algorithm, it employs several sub-swarms to explore the search space more efficiently. Each particle in a sub-swarm correct her position based on the best other positions, and the useful information is exchanged amon...

Swarms are a metaphor for complex dynamic systems. In swarms, large numbers of individuals locally interact and form non-linear, dynamic interaction networks. Ants, wasps and termites, for instance, are natural swarms whose individual and group behaviors have been evolving over millions of years. In their intricate nest constructions, the emergent effectiveness of their behaviors becomes appare...

Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization. This paper has proposed a new nature-inspired metaheuristic called Whale Swarm Algorithm for function optimization, which is inspired by the whales’ behavior of communicating with each other via ultras...

In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).