DIFERENTIAL EVOLUTION ALGORITHM FOR NONLINEAR REGRESSION MODELS

Abstract

Due to the inherent difficulties of nonlinear modelling, the studies for finding more practical methods on parameter estimation become more and more important. As well as numerical methods like Gauss-Newton, The Steepest Descent, Newton-Raphson, Levenberg-Marquardt Compromise algorithms etc., some methods based on artificial intelligence optimization get popularity among scientists increasingly. In this study, differential evolution algorithm (DEA) as one of the main artificial intelligence algorithms is used in nonlinear modelling. Then the parameter estimates by this method have been compared with those obtained by classic Gauss-Newton method. We have used three growth models, namely, Gompertz, Logistic and Weibull, in modeling. In the end, our emphasis is the similarity of parameter estimates realized by both methods. Hence DEA may be advocated for finding the similar results with greater simplicity.