Multiobjective Optimization for the Forecasting Models on the Base of the Strictly Binary Trees

The optimization problem dealing with the development of the forecasting models on the base of strictly binary trees has been considered. The aim of paper is the comparative analysis of two optimization variants which are applied for the development of the forecasting models. Herewith the first optimization variant assumes the application of one quality indicator of the forecasting model named as the affinity indicator and the second variant realizes the application of two quality indicators of the forecasting model named as the affinity indicator and the tendencies discrepancy indicator. In both optimization variants the search of the best forecasting models is carried out by means of application of the modified clonal selection algorithm. To obtain the high variety of population of the forecasting models it is offered to consider values of the crowding-distance at the realization of the second optimization variant. The results of experimental studies confirming the use efficiency of the modified clonal selection algorithm on the base of the second optimization variant are given. Keywords—forecasting model; strictly binary tree; modified clonal selection algorithm; multiobjective optimization; affinity indicator; tendencies discrepancy indicator