A novel hybrid flow direction optimizer-dynamic oppositional based learning algorithm for solving complex constrained mechanical design problems
Betül Sultan Yıldız, Nantiwat Pholdee, Pranav Mehta, Sadiq M. Sait, Sumit Kumar, Sujin Bureerat, Ali Rıza Yıldız
Abstract
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.