The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'optimisation par essaim de particules (PSO) est une méthode de recherche qui utilise un ensemble d'agents qui se déplacent dans l'espace de recherche pour trouver le minimum global d'une fonction objectif. La trajectoire de chaque particule est déterminée par une règle simple intégrant la vitesse actuelle des particules et les historiques d'exploration de la particule et de ses voisines. Depuis son introduction par Kennedy et Eberhart en 1995, le PSO a attiré de nombreux chercheurs en raison de son efficacité de recherche, même pour une fonction objectif de grande dimension avec de multiples optima locaux. La dynamique de la recherche de PSO a été étudiée et de nombreuses variantes d'amélioration ont été proposées. Cet article passe en revue les progrès de la recherche sur les PSO jusqu'à présent et les récentes réalisations en matière d'application à des problèmes d'optimisation à grande échelle.
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Keisuke KAMEYAMA, "Particle Swarm Optimization - A Survey" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 7, pp. 1354-1361, July 2009, doi: 10.1587/transinf.E92.D.1354.
Abstract: Particle Swarm Optimization (PSO) is a search method which utilizes a set of agents that move through the search space to find the global minimum of an objective function. The trajectory of each particle is determined by a simple rule incorporating the current particle velocity and exploration histories of the particle and its neighbors. Since its introduction by Kennedy and Eberhart in 1995, PSO has attracted many researchers due to its search efficiency even for a high dimensional objective function with multiple local optima. The dynamics of PSO search has been investigated and numerous variants for improvements have been proposed. This paper reviews the progress of PSO research so far, and the recent achievements for application to large-scale optimization problems.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1354/_p
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@ARTICLE{e92-d_7_1354,
author={Keisuke KAMEYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Particle Swarm Optimization - A Survey},
year={2009},
volume={E92-D},
number={7},
pages={1354-1361},
abstract={Particle Swarm Optimization (PSO) is a search method which utilizes a set of agents that move through the search space to find the global minimum of an objective function. The trajectory of each particle is determined by a simple rule incorporating the current particle velocity and exploration histories of the particle and its neighbors. Since its introduction by Kennedy and Eberhart in 1995, PSO has attracted many researchers due to its search efficiency even for a high dimensional objective function with multiple local optima. The dynamics of PSO search has been investigated and numerous variants for improvements have been proposed. This paper reviews the progress of PSO research so far, and the recent achievements for application to large-scale optimization problems.},
keywords={},
doi={10.1587/transinf.E92.D.1354},
ISSN={1745-1361},
month={July},}
Copier
TY - JOUR
TI - Particle Swarm Optimization - A Survey
T2 - IEICE TRANSACTIONS on Information
SP - 1354
EP - 1361
AU - Keisuke KAMEYAMA
PY - 2009
DO - 10.1587/transinf.E92.D.1354
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2009
AB - Particle Swarm Optimization (PSO) is a search method which utilizes a set of agents that move through the search space to find the global minimum of an objective function. The trajectory of each particle is determined by a simple rule incorporating the current particle velocity and exploration histories of the particle and its neighbors. Since its introduction by Kennedy and Eberhart in 1995, PSO has attracted many researchers due to its search efficiency even for a high dimensional objective function with multiple local optima. The dynamics of PSO search has been investigated and numerous variants for improvements have been proposed. This paper reviews the progress of PSO research so far, and the recent achievements for application to large-scale optimization problems.
ER -