Biological inspired optimization algorithms for cole-impedance parameters identification

This paper introduces new meta-heuristic optimization algorithms for extracting the parameters of the Cole-impedance model. It is one of the most important models providing best fitting with the measured data. The proposed algorithms inspired by nature are known as Flower Pollination Algorithm (FPA) and Moth-Flame Optimizer (MFO). The algorithms are tested over sets of both simulated and experimental data. The results are compared with other fitting algorithms such as the Non-linear least square (NLS) and Bacterial Foraging Optimization (BFO). The comparison showed a better fit in the sum of absolute error sense (SAE) which consolidate the effectiveness of these new algorithms in the extraction process.