Abstract
Parameter identification of non-linear dynamic processes, among them fermentation ones, is rather difficult and non-trivial task to be solved. Failure of conventional optimization methods to provide a satisfactory solution provokes the idea some stochastic algorithms to be tested. As such, the promising metaheuristic algorithm Cuckoo search (CS) has been adapted and applied for a first time to a parameter identification of S. cerevisiae fed-batch fermentation process model. Aiming to improve the model accuracy and the algorithm convergence time, several pre-tests adjustments of CS have been done according to the specific optimization problem. Obtained results confirm the effectiveness and efficacy of the applied CS algorithm. In addition, a comparison between CS and simple genetic algorithm, proved as successful in parameter identification of fermentation process model, has been done. Algorithms advantages and disadvantages have been outlined and the more reliable one have been distinguished.
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
References
Abdel-Baset, M., Hezam, I.: Cuckoo search and genetic algorithm hybrid schemes for optimization problems. Appl. Math. Inf. Sci. 10(3), 1185–1192 (2016)
Angelova, M., Pencheva, T.: Tuning genetic algorithm parameters to improve convergence time. Int. J. Chem. Eng. 2011, 7 (2011). https://doi.org/10.1155/2011/646917. Article ID 646917
Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Gálvez, A., Iglesias, A., Cabellos, L.: Cuckoo search with Lévy flights for weighted Bayesian energy functional optimization in global-support curve data fitting. Sci. W. J. 2014, 11 (2014). https://doi.org/10.1155/2014/138760
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, London (2006)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995). https://doi.org/10.1109/ICNN.1995.488968
Nguyen, T.T., Vo, D.N.: Modified cuckoo search algorithm for short-term hydrothermal scheduling. Electr. Power Energy Syst. 65, 271–281 (2015)
Ong, P.: Adaptive cuckoo search algorithm for unconstrained optimization. Sci. W. J. 2014. https://doi.org/10.1155/2014/943403
Pencheva, T., Roeva, O., Hristozov, I.: Functional State Approach to Fermentation Processes Modelling. Prof. M. Drinov Academic Publishing House, Sofia (2006)
Petrov, M., Ilkova, T., Vanags, J.: Modelling of a batch whey cultivation of Kluyveromyces marxianus var. lactis MC 5 with investigation of mass transfer processes in the bioreactor. Int. J. Bioautomation 19(1), S81–S92 (2015)
Roeva, O., Fidanova, S., Paprzycki, M.: Population size influence on the genetic and ant algorithms performance in case of cultivation process modeling. In: Fidanova, S. (ed.) Recent Advances in Computational Optimization. SCI, vol. 580, pp. 107–120. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-12631-9_7
Roeva, O., Pencheva, T., Tzonkov, St., Hitzmann, B.: Functional state modelling of cultivation processes: dissolved oxygen limitation state. Int. J. Bioautomation 19(1), S93–S112 (2015)
Roeva, O., Atanassova, V.: Cuckoo search algorithm for model parameter identification. Int. J. Bioautomation 20(4), 483–492 (2016)
Yang, X.-S.: Nature-Inspired Optimization Algorithms. Elsevier Inc., London (2014)
Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBIC), USA, pp. 210–214. IEEE Publications (2009)
Yang, X.-S., Deb, S.: Multiobjective cuckoo search for design optimization. Comp. Oper. Res. 40, 1616–1624 (2013)
Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12538-6_6
Acknowledgements
The work is partially supported by the National Science Fund of Bulgaria under grants DM 07/1 “Development of New Modified and Hybrid Metaheuristic Algorithms” and DN02/10 “New Instruments for Knowledge Discovery from Data, and Their Modelling”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Angelova, M., Roeva, O., Pencheva, T. (2019). Cuckoo Search Algorithm for Parameter Identification of Fermentation Process Model. In: Nikolov, G., Kolkovska, N., Georgiev, K. (eds) Numerical Methods and Applications. NMA 2018. Lecture Notes in Computer Science(), vol 11189. Springer, Cham. https://doi.org/10.1007/978-3-030-10692-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-10692-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-10691-1
Online ISBN: 978-3-030-10692-8
eBook Packages: Computer ScienceComputer Science (R0)