Abstract
Until recently, optimization was regarded as a discipline of rather theoretical interest, with limited real-life applicability due to the computational or experimental expense involved. Practical multiobjective optimization was considered almost as a utopia even in academic studies due to the multiplication of this expense. This paper discusses the idea of using surrogate models for multiobjective optimization. With recent advances in grid and parallel computing more companies are buying inexpensive computing clusters that work in parallel. This allows, for example, efficient fusion of surrogates and finite element models into a multiobjective optimization cycle. The research presented here demonstrates this idea using several response surface methods on a pre-selected set of test functions. We aim to show that there are number of techniques which can be used to tackle difficult problems and we also demonstrate that a careful choice of response surface methods is important when carrying out surrogate assisted multiobjective search.
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References
Keane, A.J.: OPTIONS manual, http://www.soton.ac.uk/~ajk/options.ps
Obayashi, S., Jeong, S., Chiba, K.: Multi-Objective Design Exploration for Aerodynamic Configurations, AIAA-2005-4666
Deb, K.: Multi-objective optimization using evolutionary algorithms. John Wiley & Sons, Ltd., New York (2003)
Zitzler, et al.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computational Journal 8(2), 125–148 (2000)
Knowles, J., Corne, D.: The Pareto archived evolution strategy: A new baseline algorithm for multiobjective optimisation. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 98–105. IEEE Service Center, Piscatway (1999)
Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms - Part II: Application example. IEEE Transactions on Systems, Man, and Cybernetics: Part A: Systems and Humans, 38–47 (1998)
Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. Journal of Global Optimization 13, 455–492 (1998)
Sobol’, I.M., Turchaninov, V.I., Levitan, Y.L., Shukhman, B.V.: Quasi-Random Sequence Generators, Keldysh Institute of Applied Mathematics, Russian Acamdey of Sciences, Moscow (1992)
Nowacki, H.: Modelling of Design Decisions for CAD. In: Goos, G., Hartmanis, J. (eds.) Computer Aided Design Modelling, Systems Engineering, CAD-Systems. LNCS, vol. 89. Springer, Heidelberg (1980)
Kumano, T., et al.: Multidisciplinary Design Optimization of Wing Shape for a Small Jet Aircraft Using Kriging Model. In: 44th AIAA Aerospace Sciences Meeting and Exhibit, Jannuary 2006, pp. 1–13 (2006)
Nain, P.K.S., Deb, K.: A multi-objective optimization procedure with successive approximate models. KanGAL Report No. 2005002 (March 2005)
Keane, A., Nair, P.: Computational Approaches for Aerospace Design: The Pursuit of Excellence (2005) ISBN: 0-470-85540-1
Leary, S., Bhaskar, A., Keane, A.J.: A derivative based surrogate model for approximating and optimizing the output of an expensive computer simulation. J. Global Optimization 30, 39–58 (2004)
Leary, S., Bhaskar, A., Keane, A.J.: A Constraint Mapping Approach to the Structural Optimization of an Expensive Model using Surrogates. Optimization and Engineering 2, 385–398 (2001)
Emmerich, M., Naujoks, B.: Metamodel-assisted multiobjective optimization strategies and their application in airfoil design. In: Parmee, I. (ed.) Proc of. Fifth Int’l. Conf. on Adaptive Design and Manufacture (ACDM), Bristol, UK, April 2004, pp. 249–260. Springer, Berlin (2004)
Giotis, A.P., Giannakoglou, K.C.: Single- and Multi-Objective Airfoil Design Using Genetic Algorithms and Artificial Intelligence. In: EUROGEN 1999, Evolutionary Algorithms in Engineering and Computer Science (May 1999)
Knowles, J., Hughes, E.J.: Multiobjective optimization on a budget of 250 evaluations. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 176–190. Springer, Heidelberg (2005)
Chafekar, D., et al.: Multi-objective GA optimization using reduced models. IEEE SMCC 35(2), 261–265 (2005)
Nain, P.: A computationally efficient multi-objective optimization procedure using successive function landscape models. Ph.D. dissertation, Department of Mechanical Engineering, Indian Institute of Technology (July 2005)
Voutchkov, I.I., Keane, A.J.: Multiobjective optimization using surrogates. In: Proc. 7th Int. Conf. Adaptive Computing in Design and Manufacture (ACDM 2006), Bristol, pp. 167–175 (2006) ISBN 0-9552885-0-9
Keane, A.J.: Bump: A Hard (?) Problem (1994), http://www.soton.ac.uk/~ajk/bump.html
Forrester, A., Sobester, A., Keane, A.: Engineering design via Surrogate Modelling. Wiley, Chichester (2008)
Yuret, D., Maza, M.: Dynamic hill climbing: Overcoming the limitations of optimization techniques. In: The Second Turkish Symposium on Artificial Intelligence and Neural Networks, pp. 208–212 (1993)
OptionsMatlab & OptionsNSGA2_RSM, http://argos.e-science.soton.ac.uk/blogs/OptionsMatlab/
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Voutchkov, I., Keane, A. (2010). Multi-Objective Optimization Using Surrogates. In: Tenne, Y., Goh, CK. (eds) Computational Intelligence in Optimization. Adaptation, Learning, and Optimization, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12775-5_7
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DOI: https://doi.org/10.1007/978-3-642-12775-5_7
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