Paper Info
Title
Modelling group-foraging behaviour with particle swarms
Abstract
Despite the many features that the behaviour of the standard particle swarm algorithm shares with grouping behaviour in animals (e.g. social attraction and communication between individuals), this biologically inspired technique has been mainly used in classical optimisation problems (i.e. finding the optimal value in a fitness landscape). We present here a novel application for particle swarms: the simulation of group-foraging in animals. Animals looking for food sources are modelled as particles in a swarm moving over an abstract food landscape. The particles are guided to the food by a smell (or aura), which surrounds it and whose intensity is proportional to the amount of food available. The results show that this new extended version of the algorithm produces qualitatively realistic behaviour. For example, the simulation shows the emergence of group-foraging behaviour among particles.
Year
DOI
Venue
2006
10.1007/11844297_67
PPSN
Keywords
Field
DocType
qualitatively realistic behaviour,group-foraging behaviour,grouping behaviour,particle swarm,new extended version,standard particle swarm algorithm,food source,fitness landscape,abstract food landscape,classical optimisation problem
Mathematical optimization,Fitness landscape,Swarm behaviour,Evolutionary algorithm,Computer science,Parallel algorithm,Swarm intelligence,Artificial neural network,Foraging,Particle
Conference
Volume
ISSN
ISBN
4193
0302-9743
3-540-38990-3
Citations 
PageRank 
References 
4
0.88
2
Authors
3
Name
Order
Citations
PageRank
Cecilia Di Chio125121.24
Riccardo Poli22589308.79
Paolo Di Chio3225.36