Research Area: Metaheuristic Computing
n this investigation, a cellular version of a recent spot-lighted metaheuristic called The Great Salmon Run (TGSR) algorithm is developed for evolving the architecture of Artificial Neural Network (ANN). The main motivation behind the current research is to find out whether the proposed metaheuristic algorithm is able to cope with difficulties associated with designing an accurate and robust neural black-box identifier. To attest the applicability of the proposed method, the resulted strategy is applied to a real-life challenging identification problem, i.e. identifying the exhaust gas temperature (Texh) and engine-out hydrocarbon emission (HCraw) during the coldstart operation of an automotive engine. Generally, the coldstart operation is regarded as a highly non-linear, uncertain and transient phenomenon which in turn can be a very good problem for verifying the authenticity of the proposed hybrid identification strategy. Through the conducted experiments, it is proved that the proposed identification strategy can be used to identify the main operating parameters of coldstart phenomenon.
Keywords:
cellular computing
the great salmon run
TSGR
neural black-box identifier design
vehicle cold start
automotive engines
metaheuristics
artificial neural networks
ANNs
exhaust gas temperature
hydrocarbon emissions
hybrid identification
vehicle emissions
Author(s) Name: Ahmad Mozaffari and Nasser L. Azad
Journal name: International Journal of Computer Applications in Technology
Conferrence name:
Publisher name: InderScience
DOI: 10.1504/IJCAT.2016.077799
Volume Information: vol 54, No. 1
