Multi-Objective parameter estimation problems: an improved strategy

Abstract

A multi-objective optimization approach has been applied to solve parameter estimation problems. An improved algorithm, based on evolutionary strategies, has been proposed to optimize mathematical model parameters. The algorithm makes use of a new concept of fitness function, which determines the reproduction ratio as a function of the population density, and a new class of operators, which enhance the algorithm performance. Two processes have been analyzed: a grain cooling process and a grain drying process. In order to estimate the coefficient of heat transfer and the drying rate parameters of these models, minimization of the sum of the least squares of temperature and equilibrium moisture content have been conducted. Experimental data obtained from the soybean cooling in a continuous cross-flow moving bed heat exchanger and the corn drying in a fixed bed dryer have been used to evaluate the estimated parameters. The simulated results demonstrated the algorithm efficiency to perform parameter estimation. The validated model consistently fits the experimental data.