A Comparative Performance Investigation of Swarm Optimisers on the Design of Hydrostatic Thrust Bearing


SCIENTIFIC PROGRAMMING, vol.2020, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 2020
  • Publication Date: 2020
  • Doi Number: 10.1155/2020/8856770
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Gazi University Affiliated: Yes


In the design of hydrostatic thrust bearings, power loss that occurs during operation is an important parameter that affects the design, and due to such features, it falls within the interest of design optimisation studies. The fact that the decimal places of the constraints and design variables used for minimum power loss optimisation of hydrostatic thrust bearings are highly effective on the result is a challenge for the design optimisation studies carried out on the problem and has yet made it rather attractive for the researchers. In this study, it is this feature of the problem that makes it the most important motivator in researching the performance of different metaheuristic optimisers in solving the minimum power loss problem. To this end, 7 different optimisers, four of them for the first time, were applied under equal conditions with various pop sizes and a number of iterations, and their performances were addressed under this challenging benchmark problem. The performances of these methods were compared to each other. In addition to the success of optimisers in reaching a solution, their performance in different populations and iterations is also discussed in the study. Considering the results, it is seen that MVO is the most effective optimiser in solving the problem and is followed by the WOA, PSO, and GWO. The application of WOA, MVO, CS, and SSA, for the first time, on the problem has exhibited that these methods could be used in optimisation of such delicate engineering problems.