A DETERMINISTIC GLOBAL DESIGN OPTIMIZATION METHOD FOR NONCONVEX GENERALIZED POLYNOMIAL PROBLEMS

A new design optimization method is described for finding global solut ions of models with a nonconvex objective function and nonlinear const raints. All functions are assumed to be generalized polynomials. By in troducing new variables, the original model is transformed into one wi th a linear objective function, one convex and one reversed convex con straint. A two-phase algorithm that includes global feasible search an d focal optimal search is used for globally optimizing the transformed model. Several examples illustrate the method.