An Effective Hybrid Metaheuristic Approach Based on the Genetic Algorithm

Olympia Roeva (), Dafina Zoteva, Gergana Roeva, Maya Ignatova and Velislava Lyubenova
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Olympia Roeva: Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 105, 1113 Sofia, Bulgaria
Dafina Zoteva: Department of Computer Informatics, Faculty of Mathematics and Informatics, Sofia University “St. Kliment Ohridski”, 1164 Sofia, Bulgaria
Gergana Roeva: Department of Mechatronic Bio/Technological Systems, Institute of Robotics, Bulgarian Academy of Science, Acad. G. Bonchev Str., bl. 2, 1113 Sofia, Bulgaria
Maya Ignatova: Department of Mechatronic Bio/Technological Systems, Institute of Robotics, Bulgarian Academy of Science, Acad. G. Bonchev Str., bl. 2, 1113 Sofia, Bulgaria
Velislava Lyubenova: Department of Mechatronic Bio/Technological Systems, Institute of Robotics, Bulgarian Academy of Science, Acad. G. Bonchev Str., bl. 2, 1113 Sofia, Bulgaria

Mathematics, 2024, vol. 12, issue 23, 1-16

Abstract: This paper presents an effective hybrid metaheuristic algorithm combining the genetic algorithm (GA) and a simple algorithm based on evolutionary computation. The evolutionary approach (EA) is applied to form the initial population of the GA, thus improving the algorithm’s performance, especially its convergence speed. To assess its effectiveness, the proposed hybrid algorithm, the EAGA, is evaluated on selected benchmark functions, as well as on a real optimisation process. The EAGA is used to identify parameters in a nonlinear system of differential equations modelling an E. coli fed-batch fermentation process. The obtained results are compared against published results from hybrid metaheuristic algorithms applied to the selected optimisation problems. The EAGA hybrid outperforms the competing algorithms due to its effective initial population generation strategy. The risk of premature convergence is reduced. Better numerical outcomes are achieved. The investigations validate the potential of the proposed hybrid metaheuristic EAGA for solving real complex nonlinear optimisation tasks.

Keywords: genetic algorithm; evolutionary algorithm; hybrid; modelling; optimisation; benchmark functions; E. coli fermentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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