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

Abstract

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.

Modeling and Multi-Objective Optimization of NOx Conversion Efficiency and NH3 Slip for a Diesel Engine

“…The NSGA-II genetic algorithm was used for the multi-objective optimization involving the maximization of NO x conversion efficiency while minimizing NH 3 slip because it has proven to give better optimal results compared with other optimization methods as observed in references [23,24].…”

“…The support vector machine (SVM), which is established based on the structural risk minimization principle, has proven to exhibit better generalization performance than neural networks and other methods [22]. In the work of Martinez-Moraleset [23], artificial neural networks were used for the non-linear identification of a gasoline engine for the purpose of evaluating objective functions used within an optimization framework involving the use of the NSGA-II genetic algorithm and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. It was observed that the NSGA-II algorithm performed better than the MOPSO algorithm in the Pareto based optimization process.…”

A multi-principle module identification method for product platform design

“…Recently, evolution multiobjective optimization, which applies evolution computation to multiobjective optimization has attracted a great deal of attention (Feng et al, 2010;Cheng et al, 2013;Martínez-Morales et al, 2013;Gao et al, 2014). Many multiobjective genetic algorithms have been proposed.…”

An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles

“…In addition to filter algorithms, there exists an abundance of algorithms for RUL estimation based on statistical theory and pattern recognition, such as Bayesian theory [25][26][27], neural networks (NN) and various transformers [16,28], support vector machine (SVM) algorithms [19,25,29,30], etc. The NN and SVM algorithms both have remarkable advantages in dealing with nonlinear modeling problems.…”