swMATH ID: 33394
Software Authors: Paulo Paneque Galuzio, Emerson Hochsteiner de Vasconcelos Segundo, Leandro dos Santos Coelho, Viviana Cocco Mariani
Description: MOBOpt - multi-objective Bayesian optimization. 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.
Homepage: https://www.sciencedirect.com/science/article/pii/S2352711020300911
Source Code: https://github.com/ppgaluzio/MOBOpt
Dependencies: Python
Keywords: SoftwareX publication; Python; Optimization problems; Multi-objective optimization; Bayesian optimization algorithm; Pareto
Related Software: GenConstraint; GPflowOpt; BayesianOptimization; DEAP; SciPy; Scikit; ParEGO; Isula; OPTool; Python
Cited in: 0 Publications

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