Analyse d'un algorithme d'intelligence en essaim pour le fourragement
Identifieur interne : 004561 ( Main/Merge ); précédent : 004560; suivant : 004562Analyse d'un algorithme d'intelligence en essaim pour le fourragement
Auteurs : Amine Boumaza [France] ; Bruno Scherrer [France]Source :
- Revue d'intelligence artificielle [ 0992-499X ] ; 2008.
Descripteurs français
- Pascal (Inist)
- Wicri :
- topic : Intelligence artificielle.
English descriptors
- KwdEn :
Abstract
We present a swarm intelligence algorithm that solves a discrete foraging problem. We describe simulations and provide a complete convergence analysis: we show that the population computes the solution of some optimal control problem and that its dynamics converges. We discuss the rate of convergence with respect to the number of agents: we give experimental and theoretical arguments that suggest that this convergence rate is superlinear with respect to the number of agents. Furthermore, we explain how this model can be extended to the case where the state space is continuous, and in order to solve optimal control problems in general. We argue that such an approach can be applied to any problem that involves the computation of the fixed point of a contraction mapping. This allows to design a large class of formally well understood swarm intelligence algorithms.
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Pascal:09-0030850Le document en format XML
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<term>Multiagent system</term>
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<front><div type="abstract" xml:lang="en">We present a swarm intelligence algorithm that solves a discrete foraging problem. We describe simulations and provide a complete convergence analysis: we show that the population computes the solution of some optimal control problem and that its dynamics converges. We discuss the rate of convergence with respect to the number of agents: we give experimental and theoretical arguments that suggest that this convergence rate is superlinear with respect to the number of agents. Furthermore, we explain how this model can be extended to the case where the state space is continuous, and in order to solve optimal control problems in general. We argue that such an approach can be applied to any problem that involves the computation of the fixed point of a contraction mapping. This allows to design a large class of formally well understood swarm intelligence algorithms.</div>
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