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PERFORMANCE OF A REAL CODED GENETIC ALGORITHM FOR THE CALIBRATION OF SCALAR CONSERVATION LAWS

Published online by Cambridge University Press:  01 July 2016

S. BERRES*
Affiliation:
Departamento de Ciencias Matemáticas y Físicas, Facultad de Ingeniería, Universidad Católica de Temuco, Temuco, Chile email [email protected]
A. CORONEL
Affiliation:
GMA, Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad del Bío-Bío, Campus Fernando May, Chillán, Chile email [email protected]
R. LAGOS
Affiliation:
Departamento de Matemática y Física, Facultad de Ciencias, Universidad de Magallanes, Punta Arenas, Chile email [email protected]
M. SEPÚLVEDA
Affiliation:
CI$^{2}$MA and DIM, Universidad de Concepción, Concepción, Chile email [email protected]
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Abstract

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This paper deals with the flux identification problem for scalar conservation laws. The problem is formulated as an optimization problem, where the objective function compares the solution of the direct problem with observed profiles at a fixed time. A finite volume scheme solves the direct problem and a continuous genetic algorithm solves the inverse problem. The numerical method is tested with synthetic experimental data. Simulation parameters are recovered approximately. The tested heuristic optimization technique turns out to be more robust than classical optimization techniques.

Type
Research Article
Copyright
© 2016 Australian Mathematical Society 

References

Akin, S. and Demiral, B., “Genetic algorithm for estimating multiphase flow functions from unsteady-state displacement experiments”, Comput. Geosci. 24 (1998) 251258 ;doi:10.1016/S0098-3004(97)00129-5.Google Scholar
Berres, S., Bürger, R., Coronel, A. and Sepúlveda, M., “Numerical identification of parameters for a strongly degenerate convection-diffusion problem modelling centrifugation of flocculated suspensions”, Appl. Numer. Math. 52 (2005) 311337 ; doi:10.1016/j.apnum.2004.08.002.Google Scholar
Bürger, R., Coronel, A. and Sepúlveda, M., “A numerical descent method for an inverse problem of a scalar conservation law modelling sedimentation”, in: Numerical mathematics and advanced applications: numerical mathematics and advanced applications (Springer, 2008) 225232 ; doi:10.1007/978-3-540-69777-0_26.Google Scholar
Bürger, R., Coronel, A. and Sepúlveda, M., “Numerical solution of an inverse problem for a scalar conservation law modelling sedimentation”, in: Hyperbolic problems: theory, numerics and applications, Volume 67 of Proceedings of Symposia in Applied Mathematics (American Mathematical Society, Providence, RI, 2009) 445454 ; doi:10.1090/psapm/067.2/2605240.Google Scholar
Bürger, R. and Diehl, S., “Convexity-preserving flux identification for scalar conservation laws modelling sedimentation”, Inverse Problems 29 (2013) 045008 ;doi:10.1088/0266-5611/29/4/045008.Google Scholar
Castro, C. and Zuazua, E., “Flux identification for 1-d scalar conservation laws in the presence of shocks”, Math. Comput. 80 (2011) 20252070 ; doi:10.1007/978-0-387-36797-2.Google Scholar
Coello, C. A., Lamont, G. L. and van Veldhuizen, D. A., Evolutionary algorithms for solving multi-objective problems, 2nd edn, Genetic and Evolutionary Computation (Springer, Berlin–Heidelberg, 2007) ; doi:10.1007/978-0-387-36797-2.Google Scholar
Connolly, T. J. and Wall, D. J. N., “On some inverse problems for a nonlinear transport equation”, Inverse Problems 13 (1997) 283295 ; doi:10.1088/0266-5611/13/2/006.Google Scholar
Constales, D., Kacur, J. and Van Keer, R., “Parameter identification by a single injectionextraction well”, Inverse Problems 18 (2002) 16051620 ; doi:10.1088/0266-5611/18/6/312.Google Scholar
Coronel, A., James, F. and Sepúlveda, M., “Numerical identification of parameters for a model of sedimentation processes”, Inverse Problems 19 (2003) 951972 ;doi:10.1088/0266-5611/19/4/311.Google Scholar
Dafermos, C. M., Hyperbolic conservation laws in continuum physics, Volume 325 of Grundlehren der Mathematischen Wissenschaften (Fundamental Principles of Mathematical Sciences) (Springer, Berlin, 2010) ; doi:10.1007/978-3-642-04048-1.Google Scholar
De Clerq, J., Nopens, I., Defrancq, J. and Vanrolleghem, Pa., “Extending and calibrating a mechanistic hindered and compression settling model for activated sludge using in-depth batch experiments”, Water Research 42 (2008) 781791 ; doi:10.1016/j.watres.2007.08.040.Google Scholar
De Jong, K. A., “An analysis of the behaviour of a class of genetic adaptive systems”, Ph. D. Thesis, University of Michigan, 1975.Google Scholar
Eymard, R., Gallouët, T. and Herbin, R., Finite volume methods, Volume VII Handbook of numerical analysis (North-Holland, Amsterdam, 2000) 7131020 ; doi:10.4249/scholarpedia.9835.Google Scholar
Fogel, L. J., Owens, A. J. and Walsh, M. J., Artificial intelligence through simulated evolution (Wiley, Chichester, 1966).Google Scholar
Goldberg, D. E., Genetic algorithms in search, optimization and machine learning, 1st edn (Addison-Wesley Longman Publishing, Boston, MA, 1989).Google Scholar
Hansen, N., Auger, A., Ros, R., Finck, S. and Pošík, P., “Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009”, in: Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO ’10 (New York, 2010) 16891696 ; doi:10.1145/1830761.1830790.Google Scholar
Haupt, R. L. and Haupt, S. E., Practical genetic algorithms, 2nd edn (Wiley-Interscience [John Wiley & Sons], Hoboken, NJ, 2004) ; doi:10.1002/0471671746.Google Scholar
Holden, H., Priuli, F. S. and Risebro, N. H., “On an inverse problem for scalar conservation laws”, Inverse Problems 30 (2014) 035015 ; doi:10.1088/0266-5611/30/3/035015.Google Scholar
Holland, J. H., “Adaptation in Natural and Artificial Systems”, (University of Michigan Press, Ann Arbor, MI, 1975) ; doi:10.1137/1018105.Google Scholar
Hou, J., Wang, D.-G., Luo, F.-Q. and Zhang, Y.-H., “A review on the numerical inversion methods of relative permeability curves”, Procedia Engineering (International Workshop on Information and Electronics Engineering) 29 (2012) 375380 ; doi:10.1016/j.proeng.2011.12.726.Google Scholar
Ingham, D. B. and Harris, S. D., Parameter identification within a porous medium using genetic algorithms, Volume VII Handbook of Porous Media, Geothermal, Manufacturing, Combustion, and Bioconvection Applications in Porous Media (CRC Press, 2005) 678742 ; doi:10.1201/9780415876384.ch17.Google Scholar
James, F. and Sepúlveda, M., “Parameter identification for a model of chromatographic column”, Inverse Problems 10 (1994) 12991314 ; doi:10.1088/0266-5611/10/6/008.Google Scholar
James, F. and Sepúlveda, M., “Convergence results for the flux identification in a scalar conservation law”, SIAM J. Control Optim. 37 (1999) 869891 ; doi:10.1137/S0363012996272722.Google Scholar
James, F., Sepúlveda, M., Charton, F., Quiñones, I. and Guiochon, G., “Determination of binary competitive equilibrium isotherms from the individual chromatographic band profiles”, Chem. Eng. Sci. 54 (1999) 16771696 ; doi:10.1016/S0009-2509(98)00539-9.CrossRefGoogle Scholar
Jebalia, M., Auger, A., Schoenauer, M., James, F. and Postel, M., “Identification of the isotherm function in chromatography using cma-es”, in: IEEE congress on evolutionary computation (2007) 42894296 ; doi:10.1109/CEC.2007.4425031.Google Scholar
Koza, J. R., Genetic programming: on the programming of computers by means of natural selection (MIT Press, Cambridge, MA, 1992) ; doi:10.1007/BF00175355.Google Scholar
Lagarias, J. C., Reeds, J. A., Wright, M. H. and Wright, P. E., “Convergence properties of the Nelder–Mead simplex method in low dimensions”, SIAM J. Optim. 9 (1998) 112147 ;doi:10.1137/S1052623496303470.Google Scholar
LeVeque, R. J., Numerical methods for conservation laws, 2nd edn Lectures in Mathematics ETH Zürich (Birkhäuser, Basel, 1992) ; doi:10.1007/978-3-0348-8629-1.Google Scholar
Lighthill, M. J. and Whitham, G. B., “On kinematic waves. II. A theory of traffic flow on long crowded roads”, Proc. R. Soc. Lond. Ser. A 229 (1955) 317345 ; doi:10.1098/rspa.1955.0089.Google Scholar
Liu, H. and Pan, T., “Interaction of elementary waves for scalar conservation laws on a bounded domain”, Math. Methods Appl. Sci. 26 (2003) 619632 ; doi:10.1002/mma.370.Google Scholar
Michalewicz, Z., Genetic algorithms + data structures = evolution programs, Artificial Intelligence (Springer, Berlin, 1992) ; doi:10.1007/978-3-662-03315-9.Google Scholar
Mikelic, A. and Tutek., Z., “Identification of mobilities for the Buckley–Leverett equation”, Inverse Problems 6 (1990) 767 ; doi:10.1088/0266-5611/6/5/007.Google Scholar
Rechenberg, I., Evolutionstrategie: Optimierung Technischer Systeme nach Prinzipien des Biologischen Evolution (Frommann-Holzboog, Stuttgart, 1973) ; doi:10.1002/fedr.19750860506.Google Scholar
Redlich, O. and Peterson, D. L., “A useful adsorption isotherm”, J. Phys. Chem. 63 (1959) 1024 ; doi:10.1021/j150576a611.Google Scholar
Rhee, H. K., Aris, R. and Amundson, N. R., “First-order partial differential equations”, in: Theory and application of hyperbolic systems of quasilinear equations, Volume II Prentice Hall Int. Ser. in Physical and Chemical Engineering Sciences (Prentice Hall, Englewood Cliffs, NJ, 1989).Google Scholar
Rocca, P., Benedetti, M., Donelli, M., Franceschini, D. and Massa, A., “Evolutionary optimization as applied to inverse scattering problems”, Inverse Problems 25 (2009) 123003 ;doi:10.1088/0266-5611/25/12/123003.Google Scholar
Schneider, J. and Kirkpatrick, S., Stochastic optimization, Scientific Computation (Springer, 2007) ; doi:10.1007/978-3-540-34560-2.Google Scholar
Sivanandam, S. N. and Deepa, S. N., Introduction to genetic algorithms (Springer, Berlin, 2008) ; doi:10.1007/978-3-540-73190-0.Google Scholar
Tang, H.-W., Xin, X.-K., Dai, W.-H and Xiao, Y., “Parameter identification for modeling river network using a genetic algorithm”, J. Hydrodynamics, Ser. B 22 (2010) 246253 ;doi:10.1016/S1001-6058(09)60051-2.Google Scholar
Toro, E. F., Riemann solvers and numerical methods for fluid dynamics: a practical introduction, 3rd edn (Springer, Berlin, 2009) ; doi:10.1007/b79761.Google Scholar
Usher, S. P., Studer, L. J., Wall, R. C. and Scales, P. J., “Characterisation of dewaterability from equilibrium and transient centrifugation test data”, Chem. Eng. Sci. 93 (2013) 277291 ;doi:10.1016/j.ces.2013.02.026.Google Scholar