Global optimization of mixed-integer nonlinear (polynomial) programming problems: the Bernstein polynomial approach

In this paper, we propose an algorithm for constrained global optimization of mixed-integer nonlinear programming (MINLP) problems. The proposed algorithm uses the Bernstein polynomial form in a branch-and-bound framework. Ingredients such as continuous relaxation, branching for integer decision variables, and fathoming for each subproblem in the branch-and-bound tree are used. The performance of the proposed algorithm is tested and compared with several state-of-the-art MINLP solvers, on two sets of test problems. The results of the tests show the superiority of the proposed algorithm over the state-of-the-art solvers in terms of the chosen performance metrics.