Abstract
Due to the good correlation between the physiological and pathological conditions of fruits and vegetables and their equivalent Cole impedance model parameters, an accurate and reliable technique for their identification is sought by many researchers since the introduction of the model in early 1940s. The nonlinear least squares (NLS) and its variants are examples of the conventional optimization techniques that are commonly used in literature to tackle this problem based on complex-valued impedance measurement data. However, as happens in most conventional techniques, the NLS and its variants are subject to falling local optimal solutions and prone to getting distracted by outliers. This motivated the authors to use six meta-heuristic optimization techniques to estimate the Cole impedance models' parameters on the basis of either complex impedance or magnitude-only impedance experimental measurements. Most of the meta-heuristic optimization algorithms under investigation are entirely new to this application. These algorithms include the: salp optimization algorithm (SSA), moth-flame optimizer (MFO), whale optimization algorithm (WOA), grey wolf optimizer (GWO), cuckoo search optimizer (CS) and flower pollination algorithm (FPA). The comparison with the NLS algorithm and most of the bio-inspired algorithms under investigation show greater consistency and accuracy with regard to the estimated parameters. A box plot statistical analysis is carried out to prove the effectiveness of the investigated bio-inspired algorithms.