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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Particle Swarm Optimization (PSO) ialah kaedah carian yang menggunakan satu set ejen yang bergerak melalui ruang carian untuk mencari minimum global bagi fungsi objektif. Trajektori setiap zarah ditentukan oleh peraturan mudah yang menggabungkan halaju zarah semasa dan sejarah penerokaan zarah dan jirannya. Sejak diperkenalkan oleh Kennedy dan Eberhart pada tahun 1995, PSO telah menarik ramai penyelidik kerana kecekapan cariannya walaupun untuk fungsi objektif dimensi tinggi dengan pelbagai optima tempatan. Dinamik carian PSO telah disiasat dan banyak varian untuk penambahbaikan telah dicadangkan. Kertas kerja ini mengkaji kemajuan penyelidikan PSO setakat ini, dan pencapaian terkini untuk aplikasi kepada masalah pengoptimuman berskala besar.
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Salinan
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
Salinan
@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},}
Salinan
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 -