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Combining artificial neural network and multi-objective optimization to reduce a heavy-duty diesel engine emissions and fuel consumption

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Abstract

Nondominated sorting genetic algorithm II (NSGA-II) is well known for engine optimization problem. Artificial neural networks (ANNs) followed by multi-objective optimization including a NSGA-II and strength pareto evolutionary algorithm (SPEA2) were used to optimize the operating parameters of a compression ignition (CI) heavy-duty diesel engine. First, a multi-layer perception (MLP) network was used for the ANN modeling and the back propagation algorithm was utilized as training algorithm. Then, two different multi-objective evolutionary algorithms were implemented to determine the optimal engine parameters. The objective of the present study is to decide which algorithm is preferable in terms of performance in engine emission and fuel consumption optimization problem.

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Authors and Affiliations

  1. School of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran

    Amir-Hasan Kakaee, Pourya Rahnama, Amin Paykani & Behrooz Mashadi

Authors
  1. Amir-Hasan Kakaee
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  2. Pourya Rahnama
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  3. Amin Paykani
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  4. Behrooz Mashadi
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Correspondence to Amin Paykani.

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Kakaee, AH., Rahnama, P., Paykani, A. et al. Combining artificial neural network and multi-objective optimization to reduce a heavy-duty diesel engine emissions and fuel consumption. J. Cent. South Univ. 22, 4235–4245 (2015). https://doi.org/10.1007/s11771-015-2972-1

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  • DOI: https://doi.org/10.1007/s11771-015-2972-1

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