Comparison of penalty functions on a penalty approach to mixed-integer optimization

By Francisco, R.B.; Costa, M.F.P.; Rocha, A.M.A.C.; Fernandes, E.M.G&a

AIP Conference Proceedings

2016

Abstract

In this paper, we present a comparative study involving several penalty functions that can be used in a penalty approach for globally solving bound mixed-integer nonlinear programming (bMIMLP) problems. The penalty approach relies on a continuous reformulation of the bMINLP problem by adding a particular penalty term to the objective function. A penalty function based on the ‘erf’ function is proposed. The continuous nonlinear optimization problems are sequentially solved by the population-based firefly algorithm. Preliminary numerical experiments are carried out in order to analyze the quality of the produced solutions, when compared with other penalty functions available in the literature.

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