Abstract
Protein Structure Prediction (PSP) is a problem of bioinformatics. It is a proven challenging problem due to two reasons, the first one is that the accuracy of prediction methods can directly affect clinical investigation, drug synthesis and curing methodologies and second one is the computational complexity of the problem, especially when the size of sequence is large. Hence, in recent years, applications of several metaheuristic algorithms have been reported in this area. The paper presents a comprehensive study of application of \(\beta \)-chaotic map enabled Grey Wolf Optimizer (\(\beta \)-GWO) and it’s comparison with other prominent variants of GWO in the protein structure prediction. A bench of artificial and real protein sequences is proposed and evaluation of the algorithm and variants is conducted. It is observed that mean free energy values obtained from (\(\beta \)-GWO) algorithm are superior to other variants of GWO and GWO itself.
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Acknowledgements
The Authors are thankful for the fully financial support from the CRS, RTU (ATU), TEQIP-III of Rajasthan Technical University, Kota, Rajasthan, India. (Project Sanction No. TEQIP-III/RTU (ATU)/CRS/2019-20/33).
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Saxena, A., Shekhawat, S., Sharma, A., Sharma, H., Kumar, R. (2021). Performance Evaluation of \(\beta \) Chaotic Map Enabled Grey Wolf Optimizer on Protein Structure Prediction. In: Gao, XZ., Kumar, R., Srivastava, S., Soni, B.P. (eds) Applications of Artificial Intelligence in Engineering. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4604-8_11
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DOI: https://doi.org/10.1007/978-981-33-4604-8_11
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