Improved scatter search for the global optimization of computationally expensive dynamic models
- First Online:
A new algorithm for global optimization of costly nonlinear continuous problems is presented in this paper. The algorithm is based on the scatter search metaheuristic, which has recently proved to be efficient for solving combinatorial and nonlinear optimization problems. A kriging-based prediction method has been coupled to the main optimization routine in order to discard the evaluation of solutions that are not likely to provide high quality function values. This makes the algorithm suitable for the optimization of computationally costly problems, as is illustrated in its application to two benchmark problems and its comparison with other algorithms.
KeywordsGlobal optimization Expensive functions Scatter search Kriging
Unable to display preview. Download preview PDF.
- Banga J.R., Moles C.G. and Alonso A.A. (2003). Global optimization of bioprocesses using stochastic and hybrid methods. In: Floudas, C.A. and Pardalos, P.M. (eds) Frontiers in Global Optimization, Nonconvex Optimization and Its Applications, vol. 74, pp 45–70. Kluwer, Hingham MA, USA Google Scholar
- Biegler L.T. and Grossmann I.E. (2004). Retrospective on optimization. Comput. Chem. Eng. 28(8): 1169–1192 Google Scholar
- (2001). The COST Simulation Benchmark—Description and Simulator Manual. COST (European Cooperation in the field of Scientific and Technical Research), Brussels Google Scholar
- Cox D.D. and John S. (1997). SDO: a statistical method for global optimization. In: Alexandrov, N. and Hussaini, M.Y. (eds) Multidisciplinary Design Optimization: State of the Art, pp 315–329. SIAM, Philadelphia Google Scholar
- Holmström K. and Edvall M.M. (2004). The tomlab optimization environment. In: Kallrath, J. and Basf, A.B. (eds) Modeling Languages in Mathematical Optimization, pp 369–378. Kluwer, Dordrecht Google Scholar
- Laguna M. and Martí R. (2003). Scatter Search: Methodology and Implementations in C. Kluwer, Boston Google Scholar
- Stein M.L. (1999). Interpolation of Spatial Data: some Theory for Kriging. Springer, New York Google Scholar
- Vazquez, E.: Modélisation comportementale de systèmes non-linéaires multivariables par méthodes à noyaux et applications. Ph.D. thesis, Paris XI Orsay University (2005)Google Scholar
- Vecchia A.V. (1998). Estimation and model identification for continuous spatial processes. J. R. Stat. Soc. B(50): 297–312 Google Scholar
- Yaglom A.M. (1986). Correlation Theory of Stationary and Related Random Functions I: Basic results. Springer Series in Statistics. Springer, New York Google Scholar
- The MathWorks Inc.: Optimization Toolbox for Use with Matlab®. User’s guide. Version 2Google Scholar