Abstract
Two efficient clustering-based genetic algorithms are developed for the optimisation of reaction rate parameters in chemical kinetic modelling. The genetic algorithms employed are used to determine new reaction rate coefficients for the combustion of four different fuel/air mixtures in a perfectly stirred reactor (PSR). The incorporation of clustering into the genetic algorithm allows for a considerable reduction in the number of computationally expensive fitness evaluations to be realised without any loss in performance. At each generation, the individuals are clustered into several groups and then only the individual that represents the cluster is evaluated using the expensive fitness function. The fitness values of the other individuals in the same cluster are estimated from the representative individual based on a distance measure in a process called fitness imitation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dixon-Lewis, G., Goldsworthy, F.A., Greenberg, J.B.: Proceedings of the Royal Society, London, A346, 261–275 (1975)
Dagaut, P., Reuillon, M., Boetner, J.C., Cathonnet, M.: Kerosene Combustion at Pressures up to 40atm: Experimental Study and Detailed Chemical Kinetic Modelling. In: Proceedings of the Combustion Institute, vol. 25, pp. 919–926 (1994)
Polifke, W., Geng, W., Döbbeling, K.D.: Optimisation of Rate Coefficients for Simplified Reaction Mechanisms with Genetic Algorithms. Combustion and Flame 113, 119–135 (1998)
Harris, S.D., Elliott, L., Ingham, D.B., Pourkashanian, M., Wilson, C.W.: The Optimisation of Reaction Rate Parameters for Chemical Kinetic Modelling of Combustion Using Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering 190, 1065–1083 (2000)
Kee, R.J., Miller, J.A., Jefferson, T.H.: CHEMKIN: A General-Purpose, Problem- Independent, Transport Table, FORTRAN Chemical Kinetics Code Package. Sandia Report SAND80-8003, Sandia National Laboratories (1980)
Glarborg, P., Kee, R.J., Grcar, J.F., Miller, J.A.: PSR: A FORTRAN Program for Modelling Well-Stirred Reactors. Sandia Report SAND86-8209, Sandia National Laboratories (1988)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)
Rasheed, K.: An Incremental-Approximate Clustering Approach for Developing Dynamic Reduced Models for Design Optimisation. In: Proceedings of the 2000 Congress on Evolutionary Computation, pp. 986–993 (2000)
Ratle, A.: Accelerating the Convergence of Evolutionary Algorithms by Fitness Landscape Approximation. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 87–96. Springer, Heidelberg (1998)
Rasheed, K., Vattam, S., Ni, X.: Comparison of Methods for Using Reduced Models to Speed Up Design Optimisation. In: Proceedings of the Genetic and Evolutionary Computation Conference 2002, pp. 1180–1187 (2002)
Gunn, S.R.: Support Vector Machines for Classification and Regression. Technical Report, University of Southampton, Faculty of Engineering and Applied Science, Department of Electronics and Computer Science (1998)
Jin, Y.: Fitness Approximation in Evolutionary Computation – A Survey. In: Proceedings of the 2002 Genetic and Evolutionary Computation Conference 2002, pp. 1105–1112 (2002)
Gose, E., Johnsonbaugh, R., Jost, S.: Pattern Recognition and Image Analysis. Prentice Hall PTR, Englewood Cliffs (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Elliott, L., Ingham, D.B., Kyne, A.G., Mera, N.S., Pourkashanian, M., Whittaker, S. (2004). Efficient Clustering-Based Genetic Algorithms in Chemical Kinetic Modelling. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_106
Download citation
DOI: https://doi.org/10.1007/978-3-540-24855-2_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22343-6
Online ISBN: 978-3-540-24855-2
eBook Packages: Springer Book Archive