An elite approach to re-design Aquila optimizer for efficient AFR system control

Davut Izci, Serdar Ekinci and Abdelazim G Hussien

PLOS ONE, 2023, vol. 18, issue 9, 1-22

Abstract: Controlling the air-fuel ratio system (AFR) in lean combustion spark-ignition engines is crucial for mitigating emissions and addressing climate change. In this regard, this study proposes an enhanced version of the Aquila optimizer (ImpAO) with a modified elite opposition-based learning technique to optimize the feedforward (FF) mechanism and proportional-integral (PI) controller parameters for AFR control. Simulation results demonstrate ImpAO’s outstanding performance compared to state-of-the-art algorithms. It achieves a minimum cost function value of 0.6759, exhibiting robustness and stability with an average ± standard deviation range of 0.6823±0.0047. The Wilcoxon signed-rank test confirms highly significant differences (p

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0291788

DOI: 10.1371/journal.pone.0291788

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