Analyse d'un algorithme d'intelligence en essaim pour le fourragement
Identifieur interne : 000285 ( PascalFrancis/Corpus ); précédent : 000284; suivant : 000286Analyse d'un algorithme d'intelligence en essaim pour le fourragement
Auteurs : Amine Boumaza ; Bruno ScherrerSource :
- Revue d'intelligence artificielle [ 0992-499X ] ; 2008.
Descripteurs français
- Pascal (Inist)
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.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
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Format Inist (serveur)
NO : | PASCAL 09-0030850 INIST |
---|---|
FT : | Analyse d'un algorithme d'intelligence en essaim pour le fourragement |
AU : | BOUMAZA (Amine); SCHERRER (Bruno) |
AF : | Equipe MAIA, LORIA Campus Scientifique BP 239/54506 Vandoeuvre-lès-Nancy/France (1 aut., 2 aut.) |
DT : | Publication en série; Niveau analytique |
SO : | Revue d'intelligence artificielle; ISSN 0992-499X; France; Da. 2008; Vol. 22; No. 6; Pp. 791-816; Abs. anglais; Bibl. 1 p. |
LA : | Français |
EA : | 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. |
CC : | 001D02C; 001D02B04 |
FD : | Intelligence artificielle; Solution optimale; Système multiagent; Planification; Commande optimale; Contrôle optimal; Point fixe; Insecte social; Formicoidea; Analyse algorithme; Intelligence en essaim; Taux convergence; Convergence numérique; Modélisation; Méthode espace état; Contraction; . |
FG : | Aculeata; Hymenoptera; Insecta; Arthropoda; Invertebrata |
ED : | Artificial intelligence; Optimal solution; Multiagent system; Planning; Optimal control; Optimal control (mathematics); Fix point; Social insect; Formicoidea; Algorithm analysis; Swarm intelligence; Convergence rate; Numerical convergence; Modeling; State space method; Contraction |
EG : | Aculeata; Hymenoptera; Insecta; Arthropoda; Invertebrata |
SD : | Inteligencia artificial; Solución óptima; Sistema multiagente; Planificación; Control óptimo; Control óptimo (matemáticas); Punto fijo; Insecto social; Formicoidea; Análisis algoritmo; Inteligencia de enjambre; Relación convergencia; Convergencia numérica; Modelización; Método espacio estado; Contracción |
LO : | INIST-21320.354000183981650040 |
ID : | 09-0030850 |
Links to Exploration step
Pascal:09-0030850Le document en format XML
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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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<server><NO>PASCAL 09-0030850 INIST</NO>
<FT>Analyse d'un algorithme d'intelligence en essaim pour le fourragement</FT>
<AU>BOUMAZA (Amine); SCHERRER (Bruno)</AU>
<AF>Equipe MAIA, LORIA Campus Scientifique BP 239/54506 Vandoeuvre-lès-Nancy/France (1 aut., 2 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
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<EA>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.</EA>
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<FG>Aculeata; Hymenoptera; Insecta; Arthropoda; Invertebrata</FG>
<ED>Artificial intelligence; Optimal solution; Multiagent system; Planning; Optimal control; Optimal control (mathematics); Fix point; Social insect; Formicoidea; Algorithm analysis; Swarm intelligence; Convergence rate; Numerical convergence; Modeling; State space method; Contraction</ED>
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