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Genetic Algorithm and Firefly Algorithm Hybrid Schemes for Cultivation Processes Modelling

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Transactions on Computational Collective Intelligence XVII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 8790))

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Abstract

In this paper two hybrid schemes using Firefly Algorithm (FA) and Genetic Algorithm (GA) are introduced. The novel hybrid meta-heuristics algorithms are realized and applied to parameter identification problem of a non-linear mathematical model of the E. coli cultivation process. This is a hard combinatorial optimization problem for which exact algorithms or traditional numerical methods does not work efficiently. A system of four 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 five parameters are estimated, namely maximum specific growth rate, two saturation constants and two yield coefficients. Based on the numerical and simulation result, it is shown that the model obtained by the proposed hybrid algorithms are highly competitive with standard FA and GA. The hybrid algorithms obtain similar objective function values compared to pure GA and FA, but using four times less population size and seven times less computation time. Thus, the hybrids have two advantages – take much less running time and required much less memory compared to standard GA and FA.

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References

  1. Abdullah, A., Deris, S., Mohamad, M.S., Hashim, S.Z.M.: A new hybrid firefly algorithm for complex and nonlinear problem. In: Omatu, S., et al. (eds.) Distributed Computing and Artificial Intelligence, pp. 673–680. Springer-Verlag, Heidelberg (2012)

    Chapter  Google Scholar 

  2. Apostolopoulos, T., Vlachos, A.: Application of the firefly algorithm for solving the economic emissions load dispatch problem. Int. J. Comb. 2011 (2011). Article ID 523806

    Google Scholar 

  3. Arndt, M., Hitzmann, B.: Feed forward/feedback control of glucose concentration during cultivation of Escherichia coli. In: 8th IFAC International Conference on Computer Applications in Biotechnology, Canada, pp. 425–429 (2001)

    Google Scholar 

  4. Atanassova, V., Fidanova, S., Popchev, I., Chountas, P.: Generalized nets, ACO algorithms and genetic algorithms. In: Karl, K., Sabelfeld, I.D. (eds.) Proceedings in Mathematics Monte Carlo Methods and Applications, De Gruyter, pp. 39–46 (2012)

    Google Scholar 

  5. Chai-ead, N., Aungkulanon, P., Luangpaiboon, P.: Bees and firefly algorithms for noisy non-linear optimisation problems. In: Proceedings of International Multiconference of Engineers and Computer Scientists, vol. 2, pp. 1449–1454 (2011)

    Google Scholar 

  6. Fidanova, S.: Hybrid heuristic algorithm for GPS surveying problem. In: Boyanov, T., Dimova, S., Georgiev, K., Nikolov, G. (eds.) NMA 2006. LNCS, vol. 4310, pp. 239–246. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Ganesan, T., Vasant, P., Elamvazuthi, I.: Hybrid neuro-swarm optimization approach for design of distributed generation power system. Neural Comput. Appl. 23(1), 105–117 (2013). doi:10.1007/s00521-012-0976-4

    Article  Google Scholar 

  8. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, London (2006)

    Google Scholar 

  9. Guangdong, H., Qun, W.: A hybrid ACO-GA on sports competition scheduling. In: Ostfeld, A. (ed.) Ant Colony Optimization - Methods and Applications, pp. 89–100. InTech, Rijeka (2011)

    Google Scholar 

  10. Holland, J.H.: Adaptation in Natural and Artificial Systems, 2nd edn. MIT Press, Cambridge (1992)

    Google Scholar 

  11. Houck, C.R., Joines, J.A., Kay, M.G.: A Genetic Algorithm for Function Optimization: A Matlab Implementation. Genetic Algorithm Toolbox Toutorial (1996). http://read.pudn.com/downloads152/ebook/662702/gaotv5.pdf

  12. Jiang, L., Ouyang, Q., Tu, Y.: Quantitative modeling of Escherichia coli chemotactic motion in environments varying in space and time. PLoS Comput. Biol. 6(4), e1000735 (2010). doi:10.1371/journal.pcbi.1000735

    Article  MathSciNet  Google Scholar 

  13. Karelina, T.A., Ma, H., Goryanin, I., Demin, O.V.: EI of the phosphotransferase system of Escherichia coli: mathematical modeling approach to analysis of its kinetic properties. J. Biophys. 2011 (2011). Article ID 579402, http://dx.doi.org/10.1155/2011/579402

  14. Li, N., Wang, S., Li, Y.: A hybrid approach of GA and ACO for VRP. J. Comput. Inf. Syst. 7(13), 4939–4946 (2011)

    Google Scholar 

  15. Nasiri, B., Meybodi, M.R.: Speciation-based firefly algorithm for optimization in dynamic environments. Int. J. Artif. Intell. 8(S12), 118–132 (2012)

    Google Scholar 

  16. Nemati, S., Basiri, M.E., Ghasem-Aghaee, N., Aghdam, M.H.: A novel ACO-GA hybrid algorithm for feature selection in protein function prediction. J. Expert Syst. Appl. Int. J. Arch. 36(10), 12086–12094 (2009)

    Article  Google Scholar 

  17. Olabiyisi, S.O., Fagbola, T.M., Omidiora, E.O., Oyeleye, A.C.: Hybrid metaheuristic feature extraction technique for solving timetabling problem. Int. J. Sci. Eng. Res. 3(8), 1–6 (2012). http://www.ijser.org

    Google Scholar 

  18. Petersen, C.M., Rifai, H.S., Villarreal, G.C., Stein, R.: Modeling Escherichia coli and its sources in an Urban Bayou with hydrologic simulation program - FORTRAN. J. Environ. Eng. 137(6), 487–503 (2011)

    Article  Google Scholar 

  19. Pohlheim, H.: Genetic and Evolutionary Algorithms: Principles, Methods and Algorithms. Genetic and Evolutionary Toolbox (2003). http://www.geattb.com/docu/algindex.html

  20. Han, T.A.: Intention recognition promotes the emergence of cooperation: a Bayesian network model. In: Han, T.A. (ed.) Intention Recognition, Commitment and Their Roles in the Evolution of Cooperation. SAPERE, vol. 9, pp. 101–114. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  21. Rodriguez, F.J., Garcia-Martinez, C., Lozano, M.: Hybrid metaheuristics based on evolutionary algorithms and simulated annealing: taxonomy, comparison, and synergy test. IEEE Trans. Evol. Comput. 16(6), 787–800 (2012)

    Article  Google Scholar 

  22. Roeva, O., Trenkova, T.: Genetic algorithms and firefly algorithms for non-linear bioprocess model parameters identification. In: Proceedings of the 4th International Joint Conference on Computational Intelligence (ECTA), Barcelona, Spain, 5–7 October 2012, pp. 164–169 (2012)

    Google Scholar 

  23. Roeva, O.: Real-World Application of Genetic Algorithms. In Tech, Rijeka (2012)

    Book  Google Scholar 

  24. Syam, W.P., Al-Harkan, I.M.: Comparison of three meta heuristics to optimize hybrid flow shop scheduling problem with parallel machines. In: WASET, vol. 62, pp. 271–278 (2010)

    Google Scholar 

  25. Tahouni, N., Smith, R., Panjeshahi, M.H.: Comparison of stochastic methods with respect to performance and reliability of low-temperature gas separation processes. Can. J. Chem. Eng. 88(2), 256–267 (2010)

    Google Scholar 

  26. Talbi, E.G.: Hybrid Metaheuristics. Studies in Computational Intelligence, vol. 434, p. 458. Springer, Heidelberg (2013)

    Google Scholar 

  27. Vasant, P.: Hybrid LS-SA-PS methods for solving fuzzy non-linear programming problems. Math. Comput. Model. 57(1–2), 180–188 (2013)

    Article  MathSciNet  Google Scholar 

  28. Vasant, P., Barsoum, N.: Hybrid pattern search and simulated annealing for fuzzy production planning problems. Comput. Math. Appl. 60(4), 1058–1067 (2010)

    Article  MATH  Google Scholar 

  29. Wang, G., Guo, L., Duan, H., Wang, H., Liu, L., Shao, M.: A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning. Sci. World J. 2012, 1–11 (2012). doi:10.1100/2012/583973

    MATH  Google Scholar 

  30. Yang, X.S.: Nature-Inspired Meta-Heuristic Algorithms. Luniver Press, Beckington (2008)

    Google Scholar 

  31. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  32. Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010a)

    Article  Google Scholar 

  33. Yousif, A., Abdullah, A.H., Nor, S.M., Abdelaziz, A.A.: Scheduling jobs on grid computing using firefly algorithm. J. Theor. Appl. Inf. Technol. 33(2), 155–164 (2011)

    Google Scholar 

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Acknowledgments

This work has been partially supported by the Bulgarian National Science Fund under the Grants DID 02/29 “Modelling Processes with Fixed Development Rules (ModProFix)” and DMU 02/4 “High quality control of biotechnological processes with application of modified conventional and metaheuristics methods”.

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Authors and Affiliations

  1. Institute of Biophysics and Biomedical Engineering, BAS, 105 Acad. G. Bonchev Str., 1113, Sofia, Bulgaria

    Olympia Roeva

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  1. Olympia Roeva
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Correspondence to Olympia Roeva .

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Editors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Melbourne, Australia and Polish Academy of Sciences, Warsaw, Poland

    Ryszard Kowalczyk

  3. Technical University of Lisbon, Lisbon, Portugal

    Ana Fred

  4. Polytechnic Institute of Sétubal, Sétubal, Portugal

    Filipe Joaquim

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Roeva, O. (2014). Genetic Algorithm and Firefly Algorithm Hybrid Schemes for Cultivation Processes Modelling. In: Nguyen, N., Kowalczyk, R., Fred, A., Joaquim, F. (eds) Transactions on Computational Collective Intelligence XVII. Lecture Notes in Computer Science(), vol 8790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44994-3_10

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  • DOI: https://doi.org/10.1007/978-3-662-44994-3_10

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