Title

A MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR THE OPTIMIZATION OF A FUZZY CLASSIFICATION SUBSYSTEM IN A SERIES HYBRID ELECTRIC VEHICLE.

Authors

Johanyák, Zsolt Csaba

Abstract

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.

Subjects

ELECTRIC vehicles; FUZZY systems; PARTICLE swarm optimization; PROBLEM solving; ITERATIVE methods (Mathematics)

Publication

Technical Gazette / Tehnički Vjesnik, 2017, Vol 24, p295

ISSN

1330-3651

Publication type

Academic Journal

DOI

10.17559/TV-20151021202802