Abstract

This paper presents a novel filled function approach for a general non-smooth box constrained global optimization problem. The idea of the filled function approach is that by utilizing a transforming function constructed at the given local minimizer of the objective function, the original problem could escape from the current local minimizer and identify an improved one. The proposed filled function contains two parameters, which can be readily adjusted at each iteration. The properties of the filled function are discussed, and a corresponding filled function algorithm is designed. Numerical experiments on several testing problems are implemented, and the preliminary computational results are also reported. Keywords-non-smooth box constrained global optimization; filled function; filled function approach; global minimizer

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