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

YILDIZ B. S., Pholdee N., Mehta P., Sait S. M., Kumar S., Bureerat S., ...Daha Fazla

MATERIALS TESTING, cilt.65, sa.1, ss.134-143, 2023 (SCI-Expanded) identifier identifier

  • 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.