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Performance comparison of recent population-based metaheuristic optimisation algorithms in mechanical design problems of machinery components
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Alkan, Bugra and Kaniappan Chinnathai, Malarvizhi (2021) Performance comparison of recent population-based metaheuristic optimisation algorithms in mechanical design problems of machinery components. Machines, 9 (12). e341. doi:10.3390/machines9120341 ISSN 2075-1702.
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WRAP-performance-comparison-recent-population-based-metaheuristic-optimisation-algorithms-mechanical-design-problems-machinery-components-Kaniappan-Chinnathai-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (3265Kb) | Preview |
Official URL: https://doi.org/10.3390/machines9120341
Abstract
The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge−Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.
| Item Type: | Journal Article | ||||||
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| Subjects: | H Social Sciences > HD Industries. Land use. Labor Q Science > QA Mathematics T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TS Manufactures |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Library of Congress Subject Headings (LCSH): | Industrial design , Product design, Engineering design -- Statistical methods, Metaheuristics , Artificial intelligence , Total quality management , Benchmarking (Management), Soft computing , Mathematical optimization | ||||||
| Journal or Publication Title: | Machines | ||||||
| Publisher: | MDPI | ||||||
| ISSN: | 2075-1702 | ||||||
| Official Date: | 8 December 2021 | ||||||
| Dates: |
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| Volume: | 9 | ||||||
| Number: | 12 | ||||||
| Article Number: | e341 | ||||||
| DOI: | 10.3390/machines9120341 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 7 February 2022 | ||||||
| Date of first compliant Open Access: | 7 February 2022 |
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