Genetic Algorithms and Firefly Algorithms for Non-linear Bioprocess Model Parameters Identification

Olympia Roeva, Tanya Trenkova

Abstract

In this paper, Firefly algorithms (FA) and Genetic algorithms (GA) are applied to parameter identification problem of a non-linear mathematical model of the E. coli cultivation process. A system of ordinary differential equations is proposed to model the growth of the bacteria, substrate utilization and acetate formation. Parameter optimization is performed using a real experimental data set from an E. coli MC4110 fed-batch cultivation process. In the considered non-linear mathematical model, the parameters that should be estimated are maximum specific growth rate, two saturation constants and two yield coefficients. Parameters of both meta-heuristics are tuned on the basis of several pre-tests according to the optimization problem considered here. Based on the numerical and simulation result, it is shown that the model obtained by the FA is more accurate and adequate than the one obtained using the GA. Presented results prove FA superiority and powerfulness in solving non-linear dynamic model of cultivation processes.

References

  1. Apostolopoulos, T. and Vlachos, A., (2011). Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem. International Journal of Combinatorics, Article ID 523806.
  2. Akpinar, S. and Bayhan, G. M., (2011). A Hybrid Genetic Aalgorithm for Mixed Model Assembly Line Balancing Problem with Parallel Workstations and Zoning Constraints. Engineering Applications of Artificial Intelligence, 24(3), 449-457.
  3. Arndt, M. and Hitzmann, B., (2001). Feed Forward/feedback Control of Glucose Concentration during Cultivation of Escherichia coli. 8th IFAC Int Conf on Comp Appl in Biotechn, Canada, 425-429.
  4. Chai-ead, N., Aungkulanon, P., Luangpaiboon, P., (2011). Bees and firefly algorithms for noisy non-linear optimisation problems. Prof. Int. Multiconference of Engineers and Computer Scientists, 2, 1449-1454.
  5. Silva, F., Sánchez Pérez, J. M., Gómez Pulido, J. A., Vega-Rodríguez, M. A., (2009). AlineaGA - A Genetic Algorithm with Local Search Optimization for Multiple Sequence Alignment. Applied Intelligence, 32(2), Springer, Berlin Heidelberg, 164-172.
  6. Goldberg, D. E., (2006). Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, London.
  7. Holland, J. H., (1992). Adaptation in Natural and Artificial Systems (2nd ed.). Cambridge, MIT Press.
  8. Jiang, L., Ouyang, Q., Tu, Y., (2010). Quantitative Modeling of Escherichia coli Chemotactic Motion in Environments Varying in Space and Time. PLoS Comput Biol, 6(4), e1000735. doi:10.1371/ journal.pcbi.1000735.
  9. Karelina, T. A., Ma, H., Goryanin, I., Demin, O. V., (2011). EI of the Phosphotransferase System of Escherichia coli: Mathematical Modeling Approach to Analysis of Its Kinetic Properties. J of Biophysics, Article ID 579402, doi:10.1155/2011/579402.
  10. Nasiri, B. and Meybodi, M. R., (2012). Speciation-based firefly algorithm for optimization in dynamic environments. Int J Artificial Intelligence, 8(S12), 118-132.
  11. Opalka, N., Brown, J., Lane, W. J., Twist, K.-A. F., Landick, R., Asturias, F. J., Darst, S. A., (2010). Complete Structural Model of Escherichia coli RNA Polymerase from a Hybrid Approach. PLoS Biol, 8(9), e1000483. doi:10.1371/journal.pbio.1000483.
  12. Paplinski, J. P., (2010). The Genetic Algorithm with Simplex Crossover for Identification of Time Delays. Intelligent Information Systems, 337-346.
  13. Petersen, C. M., Rifai, H. S., Villarreal, G. C., Stein, R., (2011). Modeling Escherichia coli and Its Sources in an Urban Bayou with Hydrologic Simulation Program -- FORTRAN, Journal of Environmental Engineering. 137(6), 487-503.
  14. Roeva, O., (2008). Parameter Estimation of a Monod-type Model based on Genetic Algorithms and Sensitivity Analysis. LNCS, Springer-Verlag Berlin Heidelberg, 4818, 601-608.
  15. Roeva, O., Kosev, K., Trenkova, T., (2010). A modified multi-population genetic algorithm for parameter identification of cultivation process models. IJCCI (ICEC) 2010, Valencia, Spain, 348-351.
  16. Skandamis, P. N. and Nychas, G. E., (2000). Development and Evaluation of a Model Predicting the Survival of Escherichia coli O157:H7 NCTC 12900 in Homemade Eggplant Salad at Various Temperatures, pHs, and Oregano Essential Oil Concentrations. AEM, 66(4), 1646-1653.
  17. Syam, W. P. and Al-Harkan, I. M., (2010). Comparison of Three Meta Heuristics to Optimize Hybrid Flow Shop Scheduling Problem with Parallel Machines. WASET, 62, 271-278.
  18. Tahouni, N., Smith, R., Panjeshahi, M. H., (2010). Comparison of Stochastic Methods with Respect to Performance and Reliability of Low-temperature Gas Separation Processes. The Canadian Journal of Chemical Engineering, 88(2), 256-267.
  19. Yang, X. S., (2008). Nature-Inspired Meta-Heuristic Algorithms, Luniver Press, Beckington, UK.
  20. Yang, X. S., (2009). Firefly algorithm for multimodal optimization, LNCS, Springer-Verlag Berlin Heidelberg, 5792, 169-178.
  21. Yang, X. S., (2010a). Firefly algorithm, stochastic test functions and design optimisation, International Journal of Bio-Inspired Computation, 2(2), 78-84.
  22. Yang, X. S., (2010b). Firefly algorithm, Levy flights and global optimization, Research and Development in Intelligent Systems XXVI, Springer, London, UK, 209- 218.
  23. Yousif, A., Abdullah, A. H., Nor, S. M., Abdelaziz, A. A., (2011). Scheduling Jobs on Grid Computing Using Firefly Algorithm, Journal of Theoretical and Applied Information Technology, 33(2), 155-164.
Download


Paper Citation


in Harvard Style

Roeva O. and Trenkova T. (2012). Genetic Algorithms and Firefly Algorithms for Non-linear Bioprocess Model Parameters Identification . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 164-169. DOI: 10.5220/0004115501640169


in Bibtex Style

@conference{ecta12,
author={Olympia Roeva and Tanya Trenkova},
title={Genetic Algorithms and Firefly Algorithms for Non-linear Bioprocess Model Parameters Identification},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)},
year={2012},
pages={164-169},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004115501640169},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)
TI - Genetic Algorithms and Firefly Algorithms for Non-linear Bioprocess Model Parameters Identification
SN - 978-989-8565-33-4
AU - Roeva O.
AU - Trenkova T.
PY - 2012
SP - 164
EP - 169
DO - 10.5220/0004115501640169