Optimization of neural network using kidney-inspired algorithm with control of filtration rate and chaotic map for real-world rainfall forecasting

A broad variety of real-world problems have been solved using multilayer perceptron (MLP) artificial neural networks (ANNs). Optimization techniques aid ANNs to select suitable weights and achieve correct results. Recently, the kidney-inspired algorithm (KA) has been proposed for optimization problems. This algorithm is based on the filtration, reabsorption, secretion, and excretion processes that take place in the kidneys of the human body. In the KA, the value of α in the filtration rate formula is a constant value in the range of [0, 1] that is set in the initialization stage of the algorithm. In this paper, an improved KA for optimization of the ANN model is presented in which the filtration rate is controlled by changing the value of α from minimum to maximum during the search process, which helps in achieving a better balance between exploration and exploitation in the algorithm. In this algorithm if more solutes are filtered and moved to filtered blood it means that the algorithm has more exploration. In contrast, if more solutes move to waste it means that more exploitation is performed by the algorithm. In addition, the separate use of three chaotic maps instead of a random number in the movement formula of the modified KA is investigated in order to assess the ability of each map to help to achieve superior results. The proposed method is tested on benchmark classification and time series prediction problems. The method is also applied to a real-world rainfall forecasting problem. The results of a statistical analysis prove the ability of the method.