Multi Strategy Search with Crow Search Algorithm

Durgut, Rafet; Aydın, Mehmet Emin

doi:10.5772/intechopen.102862

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…The anticipated overall good performance was shown by testing the hybrid on twenty well-known BTFs. Durgut and Aydin [54] proposed a multi-strategy search for CSA. An improved CSA objective function combining the branch and predicate distance was used by Sharma and Pathik [39].…”

Section: Introductionmentioning

confidence: 99%

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Roeva,

Zoteva

2023

Fermentation

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Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.

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Section: Introductionmentioning

confidence: 99%

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Roeva,

Zoteva

2023

Fermentation

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Crow Search Algorithm for Modelling an Anaerobic Digestion Process: Algorithm Parameter Influence

Roeva,

Chorukova

2024

Mathematics

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Corn steep liquor is a waste product from the process of treating corn grain for starch extraction. It is used as a substrate in anaerobic digestion with simultaneous hydrogen and methane production in a cascade of two anaerobic bioreactors. For process research and optimisation, adequate mathematical models are required. So, the authors aim to present a high-quality model of the corn steep liquor process for the sequential production of H2 and CH4. This paper proposes a technique for identifying the best mathematical model of the process using the metaheuristics crow search algorithm (CSA). The CSA was applied for the first time to mathematical modelling of the considered two-stage anaerobic digestion process, using real experimental data. Based on the analysis of the numerical data from the model parameter identification procedures, the influence of the main CSA parameters—the flight length, fl, and the awareness probability, AP—was investigated. Applying classical statistical tests and an innovative approach, InterCriteria Analysis, recommendations about the optimal CSA parameter tuning were proposed. The best CSA algorithm performance was achieved for the AP = 0.05, fl = 3.0, followed by AP = 0.10, fl = 2.5, and AP = 0.15, fl = 3.0. The optimal tuning of the CSA parameters resulted in a 29% improvement in solution accuracy. As a result, a mathematical model of the considered two-stage anaerobic digestion process with a high degree of accuracy was developed.

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Multi Strategy Search with Crow Search Algorithm

Cited by 2 publications

References 41 publications

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Crow Search Algorithm for Modelling an Anaerobic Digestion Process: Algorithm Parameter Influence

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