TUNING THE PARAMETERS OF A CLASSIFIER FOR FAULT DIAGNOSIS - Particle Swarm Optimization vs Genetic Algorithms

Cosmin Danut Bocaniala, José Sa da Costa

Abstract

This paper presents a comparison between the use of particle swarm optimization and the use of genetic algorithms for tuning the parameters of a novel fuzzy classifier. In previous work on the classifier, the large amount of time needed by genetic algorithms has been significantly diminished by using an optimized initial population. Even with this improvement, the time spent on tuning the parameters is still very large. The present comparison suggests that using particle swarm optimization may improve considerably the time needed for tuning the parameters. In this way, the fuzzy classifier becomes suitable for real world application. The result is validated by application to a fault diagnosis benchmark.

References

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Paper Citation


in Harvard Style

Bocaniala C. and Costa J. (2004). TUNING THE PARAMETERS OF A CLASSIFIER FOR FAULT DIAGNOSIS - Particle Swarm Optimization vs Genetic Algorithms . In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 972-8865-12-0, pages 157-162. DOI: 10.5220/0001143801570162


in Bibtex Style

@conference{icinco04,
author={Cosmin Danut Bocaniala and José Sa da Costa},
title={TUNING THE PARAMETERS OF A CLASSIFIER FOR FAULT DIAGNOSIS - Particle Swarm Optimization vs Genetic Algorithms},
booktitle={Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2004},
pages={157-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001143801570162},
isbn={972-8865-12-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - TUNING THE PARAMETERS OF A CLASSIFIER FOR FAULT DIAGNOSIS - Particle Swarm Optimization vs Genetic Algorithms
SN - 972-8865-12-0
AU - Bocaniala C.
AU - Costa J.
PY - 2004
SP - 157
EP - 162
DO - 10.5220/0001143801570162