Positivo University, Prof. Pedro Viriato Parigot de Souza, 5300, Zip code 81280-330, Curitiba, Brazil; Department of Mechanical Engineering, Pontifical Catholic University of Parana, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Brazil; Corresponding author at: Positivo University, Prof. Pedro Viriato Parigot de Souza, 5300, Zip code 81280-330, Curitiba, Brazil.
Emerson Hochsteiner de Vasconcelos Segundo
Department of Mechanical Engineering, Pontifical Catholic University of Parana, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Brazil
Leandro dos Santos Coelho
Department of Electrical Engineering, Federal University of Parana, Cel. Francisco Heraclito dos Santos, 100, Zip code 81531-980, Curitiba, Brazil; Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Brazil
Viviana Cocco Mariani
Department of Mechanical Engineering, Pontifical Catholic University of Parana, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Brazil; Department of Electrical Engineering, Federal University of Parana, Cel. Francisco Heraclito dos Santos, 100, Zip code 81531-980, Curitiba, Brazil
This work presents a new software, programmed as a Python class, that implements a multi-objective Bayesian optimization algorithm. The proposed method is able to calculate the Pareto front approximation of optimization problems with fewer objective functions evaluations than other methods, which makes it appropriate for costly objectives. The software was extensively tested on benchmark functions for optimization, and it was able to obtain Pareto Function approximations for the benchmarks with as many as 20 objective function evaluations, those results were obtained for problems with different dimensionalities and constraints.
