Design optimization of multilayer perceptron neural network by ant colony optimization applied to engine emissions data

A multilayer perceptron (MLP) artificial neural network (ANN) model has been optimized by the multi-objective ant colony optimization (MOACO) algorithm, which uses three objective functions. A sensitivity analysis to choose MOACO parameter values is carried out by calculating hypervolume metric, and the proposed approach adopts the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) decision method to choose final compromised solution on the Pareto front obtained from MOACO. As a result, we used the MLP-MOACO developed model to estimate the value of engine emissions of NOx in a four stroke, spark ignition (SI) gasoline engine and observed acceptable correlation coefficient (R2) of 0.99978. © 2019, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature.

ant colony optimization; artificial neural networks; emissions; engine; hypervolume; multilayer perceptron Ant colony optimization; Engines; Multilayers; Neural networks; Particulate emissions; Sensitivity analysis; Artificial neural network models; Compromised solution; Correlation coefficient; Design optimization; Hypervolume; Multi layer perceptron; Multi-layer perceptron neural networks; Vlsekriterijumska optimizacija i kompromisno resenje (VIKOR); Multilayer neural networks

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