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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: 376

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Puni tekst: hrvatski pdf 1.042 Kb

str. 295-301

preuzimanja: 958

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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

Datum izdavanja:

2.9.2017.

Podaci na drugim jezicima: hrvatski

Posjeta: 2.477 *