A Flexible Particle Swarm Optimization based on Global Best and Global Worst Information

Emre Çomak

2013

Abstract

A reverse direction supported particle swarm optimization (RDS-PSO) method was proposed in this paper. The main idea to create such a method relies that on benefiting from global worst particle in reverse direction. It offers avoiding from local optimal solutions and providing diversity thanks to its flexible velocity update equation. Various experimental studies have been done in order to evaluate the effect of variable inertia weight parameter on RDS-PSO by using of Rosenbrock, Rastrigin, Griewangk and Ackley test functions. Experimental results showed that RDS-PSO, executed with increasing inertia weight, offered relatively better performance than RDS-PSO with decreasing one. RDS-PSO executed with increasing inertia weight produced remarkable improvements except on Rastrigin function when it is compared with original PSO.

Paper Citation

in Harvard Style

Çomak E. (2013). A Flexible Particle Swarm Optimization based on Global Best and Global Worst Information . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 255-262. DOI: 10.5220/0004187602550262

in Bibtex Style

@conference{icpram13,
author={Emre Çomak},
title={A Flexible Particle Swarm Optimization based on Global Best and Global Worst Information},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={255-262},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004187602550262},
isbn={978-989-8565-41-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Flexible Particle Swarm Optimization based on Global Best and Global Worst Information
SN - 978-989-8565-41-9
AU - Çomak E.
PY - 2013
SP - 255
EP - 262
DO - 10.5220/0004187602550262