The Design of Open Engineering Systems Lab

University at Buffalo - The State University of New York

  • Increase font size
  • Default font size
  • Decrease font size

Solving Computationally Expensive Optimization Problems Using Hybrid Methods in Parallel Computing Environments

Research Area: Research Publication Year: 2000
Type of Publication: Technical Report Keywords: Multidisciplinary Design Optimization, Genetic Algorithm, Hybrid methods, parallel computing
Authors: Eddy, John; Hacker, Kurt; Lewis, Kemper
The optimization of many realistic large-scale engineering systems can be computationally expensive. The evaluation of a single design configuration 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 good areas 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
Symposium on Multidisciplinary Analysis and Optimization
Full text: AIAA-00-4864.pdf