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Tehnički vjesnik, Vol.24 No.Supplement 2 Rujan 2017.

Izvorni znanstveni članak
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 icon 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 MB) str. 295-301 preuzimanja: 45* citiraj
APA
Johanyák, Z.C. (2017). A modified particle swarm optimization algorithm for the optimization of a fuzzy classification subsystem in a series hybrid electric vehicle. Tehnički vjesnik, 24(Supplement 2). doi:10.17559/TV-20151021202802
Puni tekst: hrvatski, pdf (1 MB) str. 295-301 preuzimanja: 72* citiraj
APA
Johanyák, Z.C. (2017). Modificirani algoritam optimizacije roja čestica za optimizaciju fuzzy podsustava klasifikacije u serijskom hibridnom električnom vozilu. Tehnički vjesnik, 24(Supplement 2). doi:10.17559/TV-20151021202802

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
http://hrcak.srce.hr/186068

[hrvatski]

Posjeta: 188 *