Particle Swarm Optimization - A Survey
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.7 pp.1354-1361
- Publication Date
- 2009/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.1354
- Type of Manuscript
- Special Section INVITED PAPER (Special Section on Large Scale Algorithms for Learning and Optimization)
- Category
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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},}
Copy
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 -
English
