On Steering Swarms

The main contribution of this paper is a novel method allowing an external observer/controller to steer and guide swarms of identical and indistinguishable agents, in spite of the agents' lack of information on absolute location and orientation. Importantly, this is done via simple global broadcast signals, based on the observed average swarm location, with no need to send control signals to any specific agent in the swarm

Firefly Algorithm, Stochastic Test Functions and Design Optimisation

Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimization problems. In this paper, we show how to use the recently developed Firefly Algorithm to solve nonlinear design problems. For the standard pressure vessel design optimisation, the optimal solution found by FA is far better than the best solution obtained previously in literature. In addition, we also propose a few new test functions with either singularity or stochastic components but with known global optimality, and thus they can be used to validate new optimisation algorithms. Possible topics for further research are also discussed.Comment: 12 pages, 11 figure

Swarm robot social potential fields with internal agent dynamics

Swarm robotics is a new and promising approach to the design and control of multiagent robotic systems. In this paper we use a model for a second order non-linear system of self-propelled agents interacting via pair-wise attractive and repulsive potentials. We propose a new potential field method using dynamic agent internal states to successfully solve a reactive path-planning problem. The path planning problem cannot be solved using static potential fields due to local minima formation, but can be solved by allowing the agent internal states to manipulate the potential field. Simulation results demonstrate the ability of a single agent to perform reactive problem solving effectively, as well as the ability of a swarm of agents to perform problem solving using the collective behaviour of the entire swarm

Firefly Algorithms for Multimodal Optimization

Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research

Emergent Spirograph-like Patterns from Artificial Swarming

Computer simulations of a nonlinear planar system of first-order ODEs which we developed from a simple assumption that biological swarming is an outcome of aggregative behavior of the individuals in the swarm showed a surprising and novel outcome; the emergence of uniquely structured patterns that are intriguingly intricate, exquisite, symmetrical and regular. Some patterns look like flowers; others look like Spirograph curves and Guilloché patterns but with more intricate variations. Unlike Spirograph curves though, the swarm-induced patterns cannot be reproduced by any closed-form formula or by another pattern subjected to some resizing, translation, rotation and/or reflection