Abstract
In this present work, mechanical engineering optimization problems are solved by employing a novel optimizer (HFDO-DOBL) based on a physics-based flow direction optimizer (FDO) and dynamic oppositional-based learning. Five real-world engineering problems, viz. planetary gear train, hydrostatic thrust bearing, robot gripper, rolling bearing, and multiple disc clutch brake, are considered. The computational results obtained by HFDO-DOBL are compared with several newly proposed algorithms. The statistical analysis demonstrates the HFDO-DOBL dominance in finding optimal solutions relatively and competitiveness in solving constraint design optimization problems.
About the authors
Nantiwat Pholdee received his BE degree (Second Class Honors) in Mechanical Engineering in 2008 and his PhD degree in Mechanical Engineering in 2013 from Khon Kaen University, Khon Kaen, Thailand. His research interests include multidisciplinary design optimization, aircraft design, flight control, evolutionary computation, and finite-element analysis.
Sumit Kumar received the BE degree (Hons.) in mechanical engineering from Dr. A.P.J.AbdulKalam Technical University, Lucknow, India, in 2012, and the ME degree (Hons.) in design engineering from the Malaviya NationalInstitute of Technology (NIT), Jaipur, India, in 2015. He is currently a PhD research scholar with the College of Sciences and Engineering, Australian Maritime College, University of Tasmania, Launceston, Australia. His major research interests include metaheuristics techniques, multi-objective optimization, evolutionary algorithm, and renewable energy systems.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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