Development of a Global Optimization Methodology to Support Engineering Design and Manufacturing

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9414615 Sahinidis This award provides funding for research toward an algorithm for finding global solutions to nonconvex, nonlinear problem (NLPs) and mixed-integer problems (MINLPs). The approach is based on the solution of a sequence of convex underestimating subproblems generated by evolutionary subdivision of the search region. The key components of the algorithm are new optimality-based and feasibility-based range reduction tests. The former use known feasible solutions and perturbation results to exclude inferior parts of the search region from consideration, while the latter analyze constraints to obtain valid inequalities. The algorithm integrates these devices with an efficient local search heuristic. Nonlinear optimization is often useful in engineering design for the selection of design parameters. If successful, the algorithm could enable more optimal designs to be found more rapidly and at lower cost.