A novel hybrid flow direction optimizer-dynamic oppositional based learning algorithm for solving complex constrained mechanical design problems
MATERIALS TESTING, cilt.65, sa.1, ss.134-143, 2023 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 65 Sayı: 1
- Basım Tarihi: 2023
- Doi Numarası: 10.1515/mt-2022-0183
- Dergi Adı: MATERIALS TESTING
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex
- Sayfa Sayıları: ss.134-143
- Anahtar Kelimeler: dynamic oppositional based learning, flow direction algorithm, hydrostatic thrust bearing, mechanical design, planetary gear train, robot gripper, ENGINEERING OPTIMIZATION, CRASHWORTHINESS
- Bursa Uludağ Üniversitesi Adresli: Evet
Özet
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.