Tuning the Parameters of a Classifier for Fault Diagnosis - Particle Swarm Optimization vs Genetic Algorithms
نویسندگان
چکیده
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 the 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. This way, the fuzzy classifier becomes suitable for real world application. The result is validated by application to a fault diagnosis benchmark.
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