A modified farmland fertility optimizer for parameters estimation of fuel cell models

• Published in: Neural computing & applications 2021-09, Vol.33 (18), p.12169-12190
• Main Authors: Menesy, Ahmed S., Sultan, Hamdy M., Korashy, Ahmed, Kamel, Salah, Jurado, Francisco
• Format: Article
• Language: English
• Subjects: Agricultural land; Algorithms; Artificial Intelligence; Benchmarks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Empirical analysis; Fertility; Image Processing and Computer Vision; Mathematical models; Mechanical engineering; Optimization; Optimization techniques; Original Article; Parameter estimation; Parameter modification; Probability and Statistics in Computer Science; Proton exchange membrane fuel cells; Searching; Statistical analysis; Trigonometric functions
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-05821-1

This paper proposes a modified version of a well-known optimization technique called Farmland Fertility Optimization algorithm (FFA). The modified FFA (MFFA) is developed in order to improve the performance of conventional FFA. It is mainly based on two stages. Firstly, the Levy flights are used to enhance the local searching capability in the exploitation phase and the global searching capability in the exploration phase. Secondly sine–cosine functions are used to create different solutions which fluctuate outwards or towards the best possible solution. The developed algorithm has been validated using ten benchmark functions and three mechanical engineering benchmark optimization problems. After that, the newly developed algorithm MFFA is used for extracting the effective unknown parameters of Proton Exchange Membrane Fuel Cells (PEMFCs) models. The optimal extraction of these parameters is essential to determine an accurate semi-empirical mathematical model for PEMFC. The sum of squared errors between the experimental data and the corresponding calculated ones is adopted as the objective function. Four different commercial PEMFC stacks are used to validate the effectiveness of the developed algorithm. The results obtained by MFFA are compared with those obtained by the conventional FFA and other well-known optimization techniques. Moreover, a comprehensive statistical analysis is performed to determine the accuracy and efficiency of the developed algorithm. The results prove the reliability and superiority of the developed algorithm compared with the conventional FFA and other state-of-the-art optimizers.