Efficient Clustering-Based Genetic Algorithms in Chemical Kinetic Modelling

Lionel Elliott¹, Derek B. Ingham¹, Adrian G. Kyne², Nicolae S. Mera², Mohamed Pourkashanian³, and Sean Whittaker¹

¹Department of Applied Mathematics, University of Leeds, Leeds, LS2 9JT, UK
lionel@amsta.leeds.ac.uk
amt6dbi@amsta.leeds.ac.uk
che9sw@leeds.ac.uk

²Centre for Computational Fluid Dynamics, Energy and Resources Research Institute, University of Leeds, Leeds, LS2 9JT, UK
fueagk@sun.leeds.ac.uk
fuensm@sun.leeds.ac.uk

³Energy and Resources Research Institute, University of Leeds, Leeds, LS2 9JT, UK
fue6lib@sun.leeds.ac.uk

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.

LNCS 3103, p. 932 ff.

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