Non-linear robust identification of a greenhouse model using multi-objective evolutionary algorithms

This paper presents a non-linear climatic model (temperature and humidity), based on first-principles equations, of a greenhouse where roses are to be grown using hydroponic methods. Fitting of model parameters (15 in all) is based on measured data collected during summer in the Mediterranean area. A multi-objective optimisation procedure for estimating a set of non-linear models image (Pareto optimal), considering simultaneously several optimisation criteria, is presented. A new multi-objective evolutionary algorithm, image-MOGA, has been designed to converge towards image, a reduced but well distributed representation of image since good convergence and distribution of the Pareto front image is achieved by the algorithm. The set image can be used as the basis to choose an optimal model that offers a good trade-off among different optimality criteria that have been established. The procedure proposed is applied to the identification and validation of the greenhouse model presented in the paper.