Global optimization of protein-peptide docking by a filling function method

The problem is considered of finding a global minimum point of a given continuously differentiable function. The strategy is adopted of a sequential nonmonotone improvement of local optima. In particular, to escape the basin of attraction of a local minimum, a suitable gaussianbased filling function is constructed using the quadratic model of the objective function, and added to the objective to fill the basin. Then, a procedure is defined where some new minima are determined, and that of them with the lowest function value is selected as the subsequent restarting point, even if its basin is higher than the starting one. The algorithm is applied to a set of test functions from the literature and the numerical results are reported.