A novel hybrid flow direction optimizer-dynamic oppositional based learning algorithm for solving complex constrained mechanical design problems-Reference-Cited by-同舟云学术

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

Published:2023-01-01 Issue:1 Volume:65 Page:134-143
ISSN:0025-5300
Container-title:Materials Testing
language:en
Short-container-title:

Author:

Yildiz Betül S.¹,Pholdee Nantiwat²,Mehta Pranav³,Sait Sadiq M.⁴,Kumar Sumit⁵,Bureerat Sujin²,Yildiz Ali Riza¹

Affiliation:

1. Department of Mechanical Engineering , Bursa Uludag University, Uludağ University , Görükle bursa , Bursa , 16059 , Turkey

2. Department of Mechanical Engineering , Khon Kaen University , Khon Kaen , 40002 , Thailand

3. Department of Mechanical Engineering , Dharmsinh Desai University , Nadiad , Gujarat , 387001 , India

4. King Fahd University of Petroleum & Minerals , Dhahran , Saudi Arabia

5. University of Tasmania , Launceston , TAS , 7250 , Australia

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.

Publisher

Walter de Gruyter GmbH

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Link

https://www.degruyter.com/document/doi/10.1515/mt-2022-0183/pdf

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