Joint set-up of parameters in genetic algorithms and the artificial bee colony algorithm: an approach for cultivation pr
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METHODOLOGIES AND APPLICATION
Joint set-up of parameters in genetic algorithms and the artificial bee colony algorithm: an approach for cultivation process modelling Olympia Roeva1 • Dafina Zoteva1 • Oscar Castillo2
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In this paper, a Joint set-up procedure for tuning metaheuristic algorithms’ parameters is proposed. The approach is applied to a genetic algorithm (GA) and tested further on the artificial bee colony (ABC) algorithm. The joint influence of parameters (the crossover and mutation probabilities for GA and the number of population and limit for ABC) on the performance of the algorithms is investigated. As a case study, a model parameter identification of an E. coli fed-batch cultivation process is considered. E. coli is one of the most commonly used bacteria for producing medical substances in the pharmaceutical industry. The development of an effective model of a fed-batch cultivation process is very important. The processes in a bioreactor are usually described by a system of parametric nonlinear differential equations. The model parameter identification is a difficult optimization problem, which cannot be solved by applying traditional numerical methods. Feasibilities of GA and ABC for a model parameter identification of a nonlinear fed-batch cultivation process based on real experimental data are presented. The application of the proposed Joint set-up approach leads to a significant improvement in the performance of GA and ABC. As a result, a reasonable enhancement of the E. coli cultivation model accuracy is achieved. The main advantage of the tuning procedure, which searches an optimal set of values of GA and ABC control parameters, focusing on promising intervals of variation of the parameter values and refining their ranges, is that the computational efforts are reduced by more than 60% for the ABC algorithm and more than 90% for GA. Keywords E. coli fed-batch cultivation Parameter identification Genetic algorithm Artificial bee colony Parameter set-up Crossover probability Mutation probability Population size Limit parameter
1 Introduction Biotechnology has a great impact on many different areas of life, including industry and agriculture, by improving both quantity and quality of delivered products (Tylecote 2019; Zacchigna and Giacca 2018; Gartland and Gartland 2018; Morrison and La¨hteenma¨ki 2016). One of the main biotechnological instruments, a subject of particular interest of our work, is the microorganisms. Multiple types of useful and valuable microorganisms are broadly accessible in nature, like fungi, yeasts and bacteria. However, when Communicated by V. Loia. & Oscar Castillo [email protected] 1
Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
2
Tijuana Institute of Technology, Tijuana, BC, Mexico
such microorganisms are required in industrial quantities, they have to be ‘‘grown’’. Hence, the i
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