Solving computationally expensive optimization problems using hybrid methods in parallel computing environments

The optimization of many realistic large-scale engineering systems can be computationally expensive. The evaluation of a single designconfiguration can take minutes or hours, and although computing power is steadily increasing, the complexity of the analysis codes continues to keep pace. In this paper we propose a method to utilize parallel processing and hybrid optimization methods to allow for rapid solution to these complex problems. In the first stage of the hierarchical approach developed in this paper, potentially goodareas of the design space are identified with a parallel Genetic Algorithm (GA). In the second stage, the best designs within these regions are identified by either heuristic or gradient based optimization techniques. To demonstrate the usefulness of this approach preliminary results are presented from a case study involving the solution of a benchmark optimization problem.