Abstract
In this paper we present some results of continuing research into improving robustness speed and application of Hierarchical Parallel Asynchronous Evolution Algorithms (HAPEA) to multidisciplinary design optimisation (MDO) and aircraft conceptual design problems. The formulation and implementation of the HAPEA-MDO algorithm is described and can be regarded as an architecture that is applicable to either integrated or distributed system optimisation design for complex, non-linear and non-differentiable problems. In this paper the formulation for HAPEA-MDO will be described and applied to single and multi objective MDO problems. Two cases related to aircraft design are analysed. We compute the Nash and Pareto optimal configurations satisfying the specified criteria in both cases and show that the HAPEA approach provides very efficient solutions to the stated design problems.
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References
Dasgupta D, Michalewicz Z, (1997) Evolutionary Algorithms in Engineering Applications. Springer-Verlag, Berlin, Heidelberg.
Obayashi S, (1998) Multidisciplinary Design Optimization of Aircraft Wing Planform Based on Evolutionary Algorithms. Procs. IEEE International Conference on Systems, Man, and Cybernetics 1998, La Jolla, California.
Parmee I C and Watson A H, (1999) Preliminary Airframe Design Using Co-Evolutionary Multiobjective Genetic Algorithms. Procs. Genetic and Evolutionary Computation Conference, 2:1657–1665. W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela and R. E. Smith, editors, Orlando, Florida, USA, Morgan Kaufmann.
Raymer D, (2002) Enhancing Aircraft Conceptual Design Using Multidisciplinary Optimization. Department of Aeronautics, FLYG2002-2, Stockholm, KTH.
Deb K, (2003) Multi-Objective Optimization Using Evolutionary Algorithms, Wiley.
Pareto V, (1896) Cours d’Economie Politique, Rouge, Lausanne, Switzerland.
Nash J F, (1950), Equilibrium Points in N-Person Games. In Nat. Acad. Sci, USA, 36:46–49.
Sefrioui M, (1998) Algorithmes Evolutionnaires pour le calcul scientifique.Application à l’electromagnetisme et à la mécanique des fluides numériques, PhD thesis University Pierre et Marie Curie, Paris.
Sobieski J, Haftka R T, (1996) Multidisciplinary Aerospace Design Optimization: Survey of Recent Developments, AIAA Paper No. 96-0711.
Alexandrov N M, Lewis R M, (2000). Analytical and Computational Properties of Distributed Approaches to MDO, AIAA 2000–4718.
Wakunda J, Zell A, (2000) Median-selection for parallel steady-state evolution strategies. In Marc Schoenauer, Kalyanmoy Deb, Günter Rudolph, Xin Yao, Evelyne Lutton, Juan Julian Merelo, and Hans-Paul Schwefel, editors, ParallelProblem Solving from Nature-PPSN VI, pages 405–414, Berlin, Springer.
Goldberg D, (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley.
Sefrioui M, Périaux J, Ganascia J-G, (1996) Fast Convergence Thanks to Diversity, L. J. Fogel and P. J. Angeline and T. Back editors, Procs. Fifth Annual Conference on Evolutionary Programming 1996, San Diego, California, IEEE Computer Society Press, MIT Press.
Coello C, Christiansen A, (1998) Two New GA-Based for Multiobjective Optimisation, In Civil Engineering Systems.
Hansen N, Ostermeier A, (2001) Completely Derandomized Self-Adaptation in Evolution Strategies, In Evolutionary Computation, 9(2): 159–195.
Cantu-Paz E, (2000) Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Pub.
Geist A, Beguelin A, Dongarra J, Jiang W, Manchek R and Sunderam V, (1994). PVM: Parallel Virtual Machine. A User’s Guide and Tutorial for Networked Parallel Computing. Massachusetts Institute of Technology.
Whitney E J, (2003) A Modern Evolutionary Technique for Design and Optimisation in Aeronautics. PhD Thesis, The University of Sydney.
McCullers A, (2003) FLOPS User’s Guide, Release 6.02, NASA Langley Research Center.
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González, L.F., Whitney, E.J., Srinivas, K., Wong, K.C., Périaux, J. (2004). Multidisciplinary Aircraft Conceptual Design Optimisation Using a Hierarchical Asynchronous Parallel Evolutionary Algorithm (HAPEA). In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture VI. Springer, London. https://doi.org/10.1007/978-0-85729-338-1_23
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DOI: https://doi.org/10.1007/978-0-85729-338-1_23
Publisher Name: Springer, London
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