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https://doi.org/10.17559/TV-20151021202802

A modified particle swarm optimization algorithm for the optimization of a fuzzy classification subsystem in a series hybrid electric vehicle

Zsolt Csaba Johanyák orcid id orcid.org/0000-0001-9285-9178 ; Department of Information Technology, Pallasz Athéné University, Izsáki út 10., H-6000, Kecskemét, Hungary


Puni tekst: engleski pdf 1.042 Kb

str. 295-301

preuzimanja: 305

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Puni tekst: hrvatski pdf 1.042 Kb

str. 295-301

preuzimanja: 868

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Sažetak

Particle swarm optimization (PSO) based optimization algorithms are simple and easily implementable techniques with low computational complexity, which makes them good tools for solving large-scale nonlinear optimization problems. This paper presents a modified version of the original method by combining PSO with a local search technique at the end of each iteration cycle. The new algorithm is applied for the task of parameter optimization of a fuzzy classification subsystem in a series hybrid electric vehicle (SHEV) aiming at the reduction of the harmful pollutant emission. The new method ensured a better fitness value than either the original PSO algorithm or the clonal selection based artificial immune system algorithm (CLONALG) by using similar parameters.

Ključne riječi

classification, fuzzy logic, hybrid electric vehicle, particle swarm optimization

Hrčak ID:

186068

URI

https://hrcak.srce.hr/186068

Podaci na drugim jezicima: hrvatski

Posjeta: 1.944 *