A Genetic Algorithm for Scheduling and Decomposition of Multidisciplinary Design Problems
Author:
Altus Stephen S.1, Kroo Ilan M.1, Gage Peter J.1
Affiliation:
1. Aircraft Aerodynamics and Design Group Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305
Abstract
Complex engineering studies typically involve hundreds of analysis routines and thousands of variables. The sequence of operations used to evaluate a design strongly affects the speed of each analysis cycle. This influence is particularly important when numerical optimization is used, because convergence generally requires many iterations. Moreover, it is common for disciplinary teams to work simultaneously on different aspects of a complex design. This practice requires decomposition of the analysis into subtasks, and the efficiency of the design process critically depends on the quality of the decomposition achieved. This paper describes the development of software to plan multidisciplinary design studies. A genetic algorithm is used, both to arrange analysis subroutines for efficient execution, and to decompose the task into subproblems. The new planning tool is compared with an existing heuristic method. It produces superior results when the same merit function is used, and it can readily address a wider range of planning objectives.
Publisher
ASME International
Subject
Computer Graphics and Computer-Aided Design,Computer Science Applications,Mechanical Engineering,Mechanics of Materials
Reference16 articles.
1. Altus, S., Kroo, I., and Gage, P., 1995, “A Genetic Algorithm for Scheduling and Decomposition of Multidisciplinary Design Problems,” 21st ASME Design Automation Conference, Boston, MA. 2. Eppinger S. D. , WhitneyD. E., SmithR. P., and GebalaD. A., 1994, “A Model-Based Method for Organizing Tasks in Product Development,” Research in Engineering Design, Vol. 6, pp. 1–13. 3. Gage, P., and Kroo, I., 1992, “Development of the Quasi-Procedural Method for Use in Aircraft Configuration Optimization,” AIAA-92-4693, 4th AIAA/USAF/NASA/OAI Symposium on Multidisciplinary Analysis and Optimization, Cleveland, OH. 4. Gill, P. E., Murray, W., Wright, M. H., 1981, Practical Optimization, Academic Press, San Diego, CA. 5. Jones, D. R., and Beltramo, M. A., “Solving Partitioning Problems with Genetic Algorithms,” Proceedings of the 4th International Conference on Genetic Algorithms, Belew, R. & Booker, L., ed. Morgan Kaufmann, San Mateo, CA.
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