Analytical development and optimization of a graphene–solution interface capacitance model

• Hediyeh Karimi,
• Rasoul Rahmani,
• Reza Mashayekhi,
• Leyla Ranjbari,
• Amir H. Shirdel,
• Niloofar Haghighian,
• Parisa Movahedi,
• Moein Hadiyan and
• Razali Ismail

Beilstein J. Nanotechnol. 2014, 5, 603–609, doi:10.3762/bjnano.5.71

• with the experimental data and the mean absolute percentage error (MAPE) is calculated to be 11.82. In order to decrease the error, a new function of E composed of α and β parameters is suggested. In another attempt, the ant colony optimization (ACO) algorithm is implemented for optimization and

• development of an analytical model to obtain a more accurate capacitance model. To further confirm this viewpoint, based on the given results, the accuracy of the optimized model is more than 97% which is in an acceptable range of accuracy. Keywords: analytical modeling; ant colony optimization (ACO

• need to be adjusted properly. Finding the best fitted values for α and β requires an optimization technique with an accurate and reliable performance. To this end, ant colony optimization (ACO) is used as one of the well-known and efficient metaheuristic swarm intelligence-based optimization algorithms