Abstract
This chapter discusses the applicability of new computational paradigms motivated by biological processes, in the realm of multidisciplinary engineering design, and particularly, in the context of using formal methods of design optimization. The computational models considered in this discussion include genetic algorithms, neural networks, and a modeling of the biological immune system. The focus of the chapter is two-fold. First, it introduces the reader to the implementation of these newly emergent methods. Second, it describes how the use of these methods alleviates some of the difficulties associated with the application of formal optimization methods in practical design problems. Such problems are typically characterized by the presence of a large number of design variables and constraints, the need to consider multiple objective criterion, and, in some cases, a fuzzy description of design specifications. The analysis associated with the multidisciplinary design problem is both complex and computationally expensive. The discussion focuses on methods to reduce the computational effort through development of efficient optimal search algorithms, and in the efficient management of couplings in the analysis problem.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Abdi, F., Ide, H., Levine, M., and Austel, L., (1988). The Art of Spacecraft Design: A Multidisciplinary Challenge. 2nd NASA/Air Force Symposium on Recent Advances in Multidisciplinary Analysis and Optimization, NASA CP-3031.
Barron, A.R., (1992). Neural Network Approximation. In Proceedings of the Seventh Yale Workshop on Adaptive and Learning Systems, Yale University, New Haven, CT, 69–72.
Bauchau, O.A., and Kang, N.K., (1993). A Multibody Formulation for Helicopter Structural Dynamic Analysis. Journal of American Helicopter Society, 38: 3–14.
Cordon, O., and Herrera, F., (1995). A General Study on Genetic Fuzzy Systems. In Periaux J., and Winter G., eds., Genetic Algorithms in Engineering and Computer Science John Wiley and Sons Limited, England.
Dorigo, M., and Schnepf, U., (1991). Organization of Robot Behaviour Through Genetic Learning Process. In Proceedings of the 5th International Conference on Advanced Robotics,The Institution of Electrical and Electronics Engineering, Pisa, Italy.
Fogel, L.J., Owens, A.J., and Walsh, M.J., (1966). Artificial Intelligence Through Simulated Evolution. Wiley Publishing, New York.
Forrest, S., (1985). Implementing Semantic Network of Structures Using the Classifier System. In Proceedings of the 1st International Conference on Genetic Algorithms Hillsdale, New Jersey, Lawrence Erlbaum Associates, 80–92.
Goel, S., and Hajela, P., (1997). Adaptive Optimization Technique Using Classifiers Based Machine Learning Paradigm. In Proceedings of the 38th AIAA/ASME/ASCE/AHS Structures, Structural Dynamics and Materials Conference Kissimmee, Florida.
Goel, S., and Hajeia, P., (1998). Turbine Aerodynamic Design Using Reinforcement Learning Optimization. The 7th AIAA/NASA/ISSMO. USAF Multidisciplinary Analysis and Optimization Meeting St. Louis, Missouri.
Goldberg, D.E., (1983). Computer-Aided Pipeline Operation Using Genetic Algorithms and Rule-Learning. Ph.D. Dissertation. University of Michigan, Ann Arbor, Michigan.
Haftka, R.T., and Gurdal, Z., (1993). Elements of Structural Optimization. Kluwer Academic Publishers, Dordrecht.
Hajela, P., (1981). Further Developments in the Controlled Growth Approach for Optimum Structural Synthesis. In Proceedings of the 12th Design Automation Conference September 12–15, Arlington, Virginia, American Society of Mechanical Engineers, Paper 82-DET-62.
Hajela, P., (1997). Stochastic Search in Discrete Structural Optimization — Simulated Annealing, Genetic Algorithms and Neural Networks. In Gutkowski W., ed., Discrete Structural Optimization Springer, New York, 55–134.
Hajela, P., and Kim, B., (1998). Classifier Systems for Enhancing Neural Network Based Global Function Approximations. In Proceedings of the 7th AIAA/NASA/ISSMO/USAF Multidisciplinary Analysis and Optimization Meeting St. Louis Missouri.
Hajela, P., and Kim, B., (1999). GA Based Learning in Cellular Automata Models for Structural Analysis. In Proceedings of the 3rd World Congress on Structural and Multidisciplinary Optimization Niagara Falls, New York.
Hajela, P., and Kim, B., (2000). On the Use of Energy Minimization for CA Based Analysis in Elasticity. In Proceedings of the 41S t AIAA/ASME/ASCE/AHS SDM Meeting, April 1–3, Atlanta, Georgia.
Hajela, P., and Lin, C.-Y., (2000). Real Versus Binary Coding in Genetic Algorithms — A Comparative Study. In Proceedings of the 5th International Conference on Computational Structures Technology, September 6–8, Leuven, Belgium.
Hardy, J., De Pazzis, O., and Pomeau, Y., (1976). Molecular Dynamics of a Classical Lattice Gas: Transport Properties and time Correlation Functions. Physics Review A13: 1949–1960.
Hecht-Nielsen, R., (1987). Counterpropagation Networks. Journal of Applied Optics 26: 4979–84.
Holland, J.H., (1962). Outline for a Logical Theory of Adaptive Systems. Journal of the Association of Computing Machinery 3: 297–314.
Holland, J.H., (1974). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, Michigan.
Holland, J.H., (1985). Properties of the Bucket-Brigade Algorithm. In Proceedings of the 1st International Conference on Genetic Algorithms Hillsdale, New Jersey, Lawrence Erlbaum Associates, 1–7.
Karr, C.L., and Gentry, E.J., (1993). Fuzzy Control of pH Using Genetic Algorithms. The Institution of Electrical and Electronics Engineering Transactions on Fuzzy Systems 1: 46–53.
Krishna Kumar, K., and Satyadas, A., (1995). Space Station Fuzzy Models and its application to an aircraft control problem. In Periaux J., and Winter G., eds., Genetic Algorithms in Engineering and Computer Science John Wiley and Sons Limited, England.
Lee, J., and Hajela, P., (2000). An Application of Classifier Systems in Improving Response Surface Based Approximations for Design Optimization. Computers and Structures, 4 to appear.
LeRiche, R., and Haftka, R.T., (1993). Optimization of Laminate Stacking Sequence for Buckling Load Maximization by Genetic Algorithms. American Institute of Aeronautics and Astronautics Journal 31: 951–956.
Lin, C.-Y., (1990). Genetic Search Methods for Multicriterion Optimal Design of Viscoelastically Damped Structures. Ph.D. Dissertation, University of Florida, Florida.
Lin, C.-Y., and Hajela, P., (1993). Genetic Search Strategies in Large Scale.Optimization. In Proceedings of the 34th AIAA/ASME/ASCE/AHS/ASC SDM Conference La Jolla, California, 2437–2447.
Orszag, S., and Yakhot, V., (1986). Reynolds Numbers Scaling of Cellular-Automaton Hydrodynamics. Physics Review Letters 56: 1691–1693.
Rechenberg, I., (1973). Evolutionsstrategie: Optimierung Technischer System nach Prinzipien der Biologischen Evolution. Frommann-Holzboog, Stuttgart.
Richards, R.A., (1995). Zeroth-Order Shape Optimization Utilizing A Learning Classifier System. Ph.D. Dissertation Stanford University, Stanford, California.
Rumelart, D.E., and McClelland, J.L., (1988). Parallel Distributed Processing. Volume 1, The MIT Press, Cambridge, Massachussets.
Rumelart, D.E., and McClelland, J.L., (1988). Parallel Distributed Processing. Volume 2, The MIT Press, Cambride, Massachussets.
Satyadas, A., and Krishna Kumar, K., (1994). Evolutionary Fuzzy Techniques for Fuzzy Controller Synthesis. In Proceedings of the First Industry/University Symposium on Research for Future Supersonic and Hypersonic Vehicles North Carolina, TSI Press, New Mexico, 148–155.
Schraudolph, N.N., and Belew, R.K., (1992). Dynamic Parameter Encoding for Genetic Algorithms. Machine Learning 9: 9–21.
Smith, R.E., Forrest, S., and Perelson, A.S., (1992). Searching for Diverse Cooperative Populations with Genetic Algorithms. Technical Report CS92–3 University of New Mexico, Department of Computer Science, Albuquerque, New Mexico.
Sobieszczanski-Sobieski, J., (1993). Multidisciplinary Design Optimization: An Emerging New Engineering Discipline. In Proceedings World Congress on Optimal Design of Structural Systems Rio de Janeiro, Brazil, August 2–6.
Szewczyk, Z., and Hajela, P., (1992). Feature Sensitive Neural Networks in Structural Response Estimation. In Proceedings of the ANNIE’92, Artificial Neural Networks in Engineering Conference November.
Tolson, R.H., and Sobieszczanski-Sobieski, J., (1985). Multidisciplinary Analysis and Synthesis: Needs and Opportunities. American Institute of Aeronautics and Astronautics Paper No. 85–0584.
Wilson, S.W., (1986). Classifier System Learning of a Boolean Function. Research Memo RIS No 27r The Rowland Institute of Science, Cambridge, Massachussets.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Wien
About this paper
Cite this paper
Hajela, P. (2001). Strategies for Modeling, Approximation, and Decomposition in Genetic Algorithms Based Multidisciplinary Design. In: Blachut, J., Eschenauer, H.A. (eds) Emerging Methods for Multidisciplinary Optimization. International Centre for Mechanical Sciences, vol 425. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2756-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-7091-2756-8_6
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83335-3
Online ISBN: 978-3-7091-2756-8
eBook Packages: Springer Book Archive