Nonlinear regression is a statistical technique widely used in research which creates models that conceptualize the relation among many variables that are related in complex forms. These models are widely used in different areas such as economics, biology, finance, engineering, etc. These models are subsequently used for different processes, such as prediction, control or optimization. Many standard regression methods have been proved that produce misleading results in certain data sets; this is especially true in ordinary least squares. In this article three metaheuristic models for parameter estimation of nonlinear regression models are described: Artificial Bee Colony, Particle Swarm Optimization and a novel hybrid algorithm ABC-PSO. These techniques were tested on 27 databases of the NIST collection with different degrees of difficulty. The experimental results provide evidence that the proposed algorithm finds consistently good results.
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