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Cuckoo Search Algorithm for Parameter Identification of Fermentation Process Model

  • Maria AngelovaEmail author
  • Olympia Roeva
  • Tania Pencheva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11189)

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.

Keywords

Cuckoo search Genetic algorithm Parameter identification Fermentation process 

Notes

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”.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Biophysics and Biomedical EngineeringBulgarian Academy of SciencesSofiaBulgaria

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