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

Yıldız, Betül SultanPholdee, NantiwatMehta, PranavSait, Sadiq M.Kumar, SumitBureerat, SujinYıldız, Ali Rıza2024-10-162024-10-162023-01-270025-5300https://doi.org/10.1515/mt-2022-0183https://www.degruyter.com/document/doi/10.1515/mt-2022-0183/htmlhttps://hdl.handle.net/11452/46538In 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.eninfo:eu-repo/semantics/closedAccessEngineering optimizationCrashworthinessDynamic oppositional based learningFlow direction algorithmHydrostatic thrust bearingMechanical designPlanetary gear trainRobot gripperScience & technologyTechnologyMaterials science, characterization & testingMaterials scienceA novel hybrid flow direction optimizer-dynamic oppositional based learning algorithm for solving complex constrained mechanical design problemsArticle00090957200001313414365110.1515/mt-2022-01832195-8572