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This work presents a new meta-heuristic approach to model structure selectionof polynomial NARX models. In this respect, the technique penalizes the modelsbased on the individual contribution of each regressor in representing thesystem. The new algorithm is tested on two experimental case studies: theidentification of an electromechanical system and a eletric heater. The resultsare compared with Error Reduction Ratio and another meta-heuristic approach.The proposed method shows its advantages over compared methods in terms of thetrade-off between prediction accuracy and model interpretability. The resultsare quantified and compared using the Mean Squared Error (MSE) indices.

Identifica\c{c}\~ao de Sistemas N\~ao Lineares Utilizando o Algoritmo  H\'ibrido e Bin\'ario de Otimiza\c{c}\~ao por Enxame de Part\'iculas e Busca  Gravitacional