JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE A 2013 Vol.14 No.9 P.657-670

Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms

Author(s): Jos D. Martnez-Morales¹, Elvia R. Palacios-Hernndez², Gerardo A. Velzquez-Carrillo³
Affiliation(s): 1. Faculty of Engineering, Autonomous University of San Luis Potosi, San Luis Potosi 78290, Mexico; more
Corresponding email(s): jdaniel.martinez@alumnos.uaslp.edu.mx
Key Words: Engine calibration, Multi-objective optimization, Neural networks, Multiple objective particle swarm optimization (MOPSO), Nondominated sorting genetic algorithm II (NSGA-II)

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José D. Martínez-Morales, Elvia R. Palacios-Hernández, Gerardo A. Velázquez-Carrillo. Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms[J]. Journal of Zhejiang University Science A, 2013, 14(9): 657-670.

@article{title="Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms",
author="José D. Martínez-Morales, Elvia R. Palacios-Hernández, Gerardo A. Velázquez-Carrillo",
journal="Journal of Zhejiang University Science A",
volume="14",
number="9",
pages="657-670",
year="2013",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1300010"
}

%0 Journal Article
%T Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms
%A José D. Martínez-Morales
%A Elvia R. Palacios-Hernández
%A Gerardo A. Velázquez-Carrillo
%J Journal of Zhejiang University SCIENCE A
%V 14
%N 9
%P 657-670
%@ 1673-565X
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1300010

TY - JOUR
T1 - Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms
A1 - José D. Martínez-Morales
A1 - Elvia R. Palacios-Hernández
A1 - Gerardo A. Velázquez-Carrillo
J0 - Journal of Zhejiang University Science A
VL - 14
IS - 9
SP - 657
EP - 670
%@ 1673-565X
Y1 - 2013
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1300010

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: In this paper, a multi-objective particle swarm optimization (MOPSO) algorithm and a nondominated sorting genetic algorithm II (NSGA-II) are used to optimize the operating parameters of a 1.6 L, spark ignition (SI) gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NO _x ), which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks (ANNs) were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms: NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NO _x , respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NO _x , respectively.

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