An Efficient Hybrid of an Ant Lion Optimizer and Genetic Algorithm for a Model Parameter Identification Problem

Olympia Roeva, Dafina Zoteva (), Gergana Roeva and Velislava Lyubenova
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Olympia Roeva: Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
Dafina Zoteva: Faculty of Mathematics and Informatics, Sofia University “St. Kliment Ohridski”, 1164 Sofia, Bulgaria
Gergana Roeva: Institute of Robotics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
Velislava Lyubenova: Institute of Robotics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria

Mathematics, 2023, vol. 11, issue 6, 1-22

Abstract: The immense application of mathematical modeling for the improvement of bioprocesses determines model development as a topical field. Metaheuristic techniques, especially hybrid algorithms, have become a preferred tool in model parameter identification. In this study, two efficient algorithms, the ant lion optimizer (ALO), inspired by the interaction between antlions and ants in a trap, and the genetic algorithm (GA), influenced by evolution and the process of natural selection, have been hybridized for the first time. The novel ALO-GA hybrid aims to balance exploration and exploitation and significantly improve its global optimization ability. Firstly, to verify the effectiveness and superiority of the proposed work, the ALO-GA is compared with several state-of-the-art hybrid algorithms on a set of classical benchmark functions. Further, the efficiency of the ALO-GA is proved in the parameter identification of a model of an Escherichia coli MC4110 fed-batch cultivation process. The obtained results have been studied in contrast to the results of various metaheuristics employed for the same problem. Hybrids between the GA, the artificial bee colony (ABC) algorithm, the ant colony optimization (ACO) algorithm, and the firefly algorithm (FA) are considered. A series of statistical tests, parametric and nonparametric, are performed. Both numerical and statistical results clearly show that ALO-GA outperforms the other competing algorithms. The ALO-GA hybrid algorithm proposed here has achieved an improvement of 6.5% compared to the GA-ACO model, 7% compared to the ACO-FA model, and 7.8% compared to the ABC-GA model.

Keywords: ant lion optimizer; genetic algorithm; parameter identification; Escherichia coli; cultivation process (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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