A novel hybrid flow direction optimizer-dynamic oppositional based learning algorithm for solving complex constrained mechanical design problems

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 thrus...

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Bibliographic Details
Published inMaterialprüfung Vol. 65; no. 1; pp. 134 - 143
Main Authors Yildiz, Betül S., Pholdee, Nantiwat, Mehta, Pranav, Sait, Sadiq M., Kumar, Sumit, Bureerat, Sujin, Yildiz, Ali Riza
Format Journal Article
LanguageEnglish
Published De Gruyter 27.01.2023
Subjects
dynamic oppositional based learning
flow direction algorithm
hydrostatic thrust bearing
mechanical design
planetary gear train
robot gripper
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ISSN0025-5300
2195-8572
DOI10.1515/mt-2022-0183

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Summary: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.
ISSN:0025-5300
2195-8572
DOI:10.1515/mt-2022-0183