Nonlinear regression in dynamic environments using particle swarm optimization

Abstract

This paper extends a PSO-based nonlinear regression technique to dynamic environments whereby the induced model dynamically adjusts when an environmental change is detected. As such, this work hybridizes a PSO designed for dynamic environments with a least-squares approximation technique to induce structurally optimal nonlinear regression models. The proposed model was evaluated experimentally and compared with the dynamic PSOs, namely multi-swarm, reinitialized, and charged PSOs, to optimize the model structure and the regression parameters in the dynamic environment. The obtained results show that the proposed model was adaptive to the changing environment to yield structurally optimal models which consequently, outperformed the dynamic PSOs for the given datasets.