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Abstrakty
The paper proposes a design procedure for the creation of a robust and effective hybrid algorithm, tailored to and capable of carrying out a given design optimisation task. In the course of algorithm creation, a small set of simple optimisation methods is chosen, out of which those performing best will constitute the hybrid algorithm. The simplicity of the method allows implementing ad-hoc modifications if unexpected adverse features of the optimisation problem are found. It is postulated to model a system that is smaller but conceptually equivalent, whose model is much simpler than the original one and can be used freely during algorithm construction. Successful operation of the proposed approach is presented in two case studies (power plant set-point optimisation and waveguide bend shape optimisation). The proposed methodology is intended to be used by those not having much knowledge of the system or modelling technology, but having the basic practice in optimisation. It is designed as a compromise between brute force optimisation and design optimisation preceded by a refined study of the underlying problem. Special attention is paid to cases where simulation failures (regardless of their nature) form big obstacles in the course of the optimisation process.
Rocznik
Tom
Strony
53--71
Opis fizyczny
Bibliogr. 23 poz., rys., tab., wykr.
Twórcy
autor
- Institute of Control and Computation Engineering, Warsaw University of Technology ul. Nowowiejska 15/19, 00–665 Warsaw, Poland, M.Kamola@ia.pw.edu.pl
Bibliografia
- [1] Bazaraa M.S., Sherali H.D. and Shetty C.M. (1993): Nonlinear Programming. Theory and Algorithms. - New York: Wiley.
- [2] Box M.J. (1965): A new method of constrained optimization and a comparison with other methods. - Comput. J., Vol. 8, No. 1, pp. 42-52.
- [3] Bujalski W. (2001): A Metod of Load Distribution in Power Systems Operatet through DCS. - Ph.D. thesis, Warsaw University of Technology, (in Polish).
- [4] Edgar T.F. and Himmelblau D.M. (1988): Optimization of Chemical Process.- New York: McGraw-Hill.
- [5] Eldred M.S., Giunta A.A., van Bloemen Vaanders B.G., Wojtkiewicz S.F.J., Hart W.E. and Alleva M.P. (2005): DAKOTA, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 3.3. Developers manual. - Tech. Rep. SAND2001-3515, Sandia Laboratories, available at http://endo.sandia. gov/DAKOTA/papers/Reference3.3.pdf
- [6] Hammel U. (1997): Simulation models, In: Handbook of Evolutionary Computation (T. Bäck, D.B. Fogel and Z.Michalewicz, Eds.).-Bristol: Institute of Physics Publishing, and Oxford, New York: Oxford University Press, pp. F1.8:1-F1.8:9.
- [7] Hyperworks (2006): Altair Hyperworks: Web page http://www.altair.com/software/hw.htm
- [8] Jankowski Z., Kurpisz Ł., Laskowski L., Łajkowski J.,Miller A., Sikora W., Portacha J. and Zgorzelski M. (1972): Mathematical model of a turboset working in changed conditions. - Bulletin Inst. Heat Engineering, Warsaw University of Technology, Vol. 33, pp. 3-36, (in Polish) (English abstract available).
- [9] Kamola M. and Malinowski K. (2000): Simulator-optimizer approach to planning of plant operation: Ill-defined simulator case. - Proc. IFAC Sympos. Manufacturing, Modeling, Management and Control, MIM, Rio Patras, Greece, pp. 377-382.
- [10] Kamola M. and Miazga P. (2001): Global and local optimization algotithms in automated wavequide design. - Proc. 5-th Nat. Conf. Evolutionary Algorithms and Global Optimization, Jastrzębia Góra, Poland, pp. 97-105.
- [11] Kamola M. (2004): Algorithms for Optimisation Problems with Implicit and Feasibility Constraints. - Ph.D. thesis, Warsaw University of Technology, available at http://www.ia.pw.edu.pl/~mkamola/Kamola04.pdf
- [12] Papalambros P.Y. (1988): Principles of Optimal Design. - Cambridge: Cambridge University Press.
- [13] Plambeck E.L., Fu B.R., Robinson S.M. and Suri R. (1996): Sample-path optimization of convex stochastic performance methods. - Math. Program., Vol. 75, No. 2, pp. 137-176.
- [14] Poloni C., Pediroda V., Clarich A. and Steven G. (2005): The use of optimisation algorithms in PSO. - Project Deliverable 4, Fenet Thematic Network, Competitive and Sustainable Growth Programme, available at http://www.fe-net.org/technology/pso/
- [15] Portacha J. (1969): Optimisation of Heat System Structure in a Steam Power Plant.- Ph.D. thesis, Warsaw University of Technology, Warsaw, (in Polish).
- [16] Press W.H., Teukolsky S.A., Vetterling W.T. and Flannery B.P. (1992): Numerical Recipes in C. - Cambridge: Cambridge University Press.
- [17] Price W.L. (1987): Global Optimization Algorithms for a CAD Workstation. - J. Optim. Theory Applic., Vol. 55, No. 1, pp. 133-146.
- [18] QWE (2003a): QuickWave-3D User's Manual. - Warsaw: QWED Ltd.
- [19] QWE (2003b): QW-Optimizer User's Manual. - Warsaw: QWED Ltd.
- [20] Sandia (2006): Features of computationally complex engineering problems. Web page http://endo.sandia.gov/DAKOTA/research/complexity.html
- [21] Scott A.T. (2001): An evaluation of three commercially available integrated design framework packages for use in the Space Systems Design Lab, Tech. Rep., Georgia Tech., available at http://www.phoenix-int.com/library/papers.php
- [22] Synaps (2003): Epogy 2003. User's Guide. - Atlanta: Synaps, Inc.
- [23] Taflove A. (1995): Computational Electrodynamics: The Finite-Difference Time-Domain Method. - Boston: Artech House.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0041-0012