Development of particle swarm clustered optimization method for applications in applied sciences

Abstract

An original particle swarm clustered optimization (PSCO) method has been developed for the implementations in applied sciences. The developed PSCO does not trap in local solutions in contrary to corresponding solutions obtained by the applications of particle swarm optimization algorithm that is frequently used in many disciplines of applied sciences. The integrations of PSCO with multilayer perceptron neural network, adaptive neuro-fuzzy inference system (ANFIS), linear equation, and nonlinear equation were applied to predict the Vistula river discharge. The performance of PSCO was also compared with autonomous groups particle swarm optimization, dwarf mongoose optimization algorithm, and weighted mean of vectors. The results indicate that the PSCO has no tendency to trap in local solutions and its global solutions are more accurate than other algorithms. The accuracy of all developed models in predicting river discharge was acceptable (R2 > 0.9). However, the derived nonlinear models are more accurate. The outcome of thirty consecutive runs shows that the derived PSCO improves the performance of machine learning techniques. The results also show that ANFIS-PSCO with RMSE = 108.433 and R2 = 0.961 is the most accurate model.