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

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

Johanyák, Zsolt Csaba