Abstract
Vast majority of practical optimization problems are of multi-objective nature. In many cases, especially if the designer’s priorities are known beforehand, the problem can be turned into a single-objective one by selecting the primary goals and handling the remaining objectives through appropriately defined constraints. Also, it is possible to aggregate the objectives into a scalar cost function using a weighted sum approach or penalty functions. In some situations, however, it is important to obtain more comprehensive information about the system at hand, in particular, to identify the best possible trade-offs between conflicting criteria. In such cases, defaulting to genuine multi-objective optimization is a necessity, which further increases the complexity of the optimization task. Perhaps the most popular multi-objective optimization approaches are population-based metaheuristics (Deb 2001). These techniques are capable of yielding the entire representation of the so-called Pareto front (Fonseca 1995) in one algorithm run; however, the computational cost of evolutionary optimization may be very high: thousands or even tens of thousands of objective function evaluations. Consequently, direct multi-objective optimization of expensive simulation models is normally prohibitive. In this chapter, we discuss multi-objective optimization of expensive models using surrogate-based optimization, particularly response correction techniques. Our considerations are illustrated using examples from the areas of microwave and antenna design as well as aerospace engineering.
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Abbott, I.H., and Von Doenhoff, A.E., Theory of Wing Sections, Dover Publications, 1959.
Agilent ADS (2011), Agilent Technologies, 1400 Fountaingrove Parkway, Santa Rosa, CA 95403–1799.
Beachkofski, B., and Grandhi, R. (2002) Improved distributed hypercube sampling. American Institute of Aeronautics and Astronautics. Paper AIAA 2002–1274.
Bekasiewicz, A., Koziel, S., Leifsson, L. (2014a) Low-cost EM-simulation-driven multi-fidelity optimization of antennas. In International Conference on Computational Science – Procedia Computer Science, 29, 769–778.
Bekasiewicz, A., Koziel, S., Zieniutycz, W. (2014b) Design Space Reduction for Expedited Multi-Objective Design Optimization of Antennas in Highly-Dimensional Spaces. In Solving Computationally Extensive Engineering Problems: Methods and Applications, Springer.
Deb, K. (2001) Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons.
FLUENT, ver. 15.0, ANSYS Inc., Southpointe, 275 Technology Drive, Canonsburg, PA 15317, 2015.
Fonseca, C.M. (1995) Multiobjective genetic algorithms with application to control engineering problems. PhD thesis, Department of Automatic Control and Systems Engineering, University of Sheffield, UK.
Hosder, S., Schetz, J.A., Mason, W.H., Grossman, B., and Haftka, R.T. (2010) Computational-Fluid-Dynamics-Based Clean-Wing Aerodynamic Noise Model for Design. Journal of Aircraft, 47, 754–762.
Kinsey, D. W., and Barth, T. J. (1984) Description of a Hyperbolic Grid Generation Procedure for Arbitrary Two-Dimensional Bodies,” AFWAL TM 84–191-FIMM.
Koziel, S. and Ogurtsov, S. (2013b) Low-cost design of SIW antennas using surrogate-based optimization. IEEE APWC.
Koziel, S., Bekasiewicz, A., and Kurgan, P. (2015) Rapid multi-objective simulation-driven design of compact microwave circuits. IEEE Microwave Wireless Comp. Letters
Leifsson, L., Koziel, S., and Hosder, S. (2015) Multi-Objective aeroacoustic shape optimization by variable-fidelity models and response surface surrogates. AIAA Modeling and Simulation Technologies Conference, Kissimee, Florida, Jan 5–9.
Lophaven, S.N., Nielsen, H.B., and Søndergaard, J., (2002) DACE: a Matlab kriging toolbox. Technical University of Denmark.
Menter, F. (1994) Two-Equation Eddy-Viscosity Turbulence Models for Engineering Applications,” AIAA Journal, 32, 1598–1605.
Spence, T. G., and Werner, D. H. (2006) A novel miniature broadband/multiband antenna based on an end-loaded planar open-sleeve dipole. IEEE Trans. Antennas Propag., 54, 3614–3620.
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Koziel, S., Leifsson, L. (2016). Multi-objective Optimization Using Variable-Fidelity Models and Response Correction. In: Simulation-Driven Design by Knowledge-Based Response Correction Techniques. Springer, Cham. https://doi.org/10.1007/978-3-319-30115-0_11
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DOI: https://doi.org/10.1007/978-3-319-30115-0_11
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