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http://hdl.handle.net/11452/29532
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Abderazek, Hammoudi | - |
dc.contributor.author | Mirjalili, Seyedali | - |
dc.date.accessioned | 2022-11-22T08:51:56Z | - |
dc.date.available | 2022-11-22T08:51:56Z | - |
dc.date.issued | 2019-11-12 | - |
dc.identifier.citation | Abderazek, H. vd. (2020). "Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism". Knowledge-Based Systems, 191. | en_US |
dc.identifier.issn | 0950-7051 | - |
dc.identifier.uri | https://doi.org/10.1016/j.knosys.2019.105237 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0950705119305568 | - |
dc.identifier.uri | http://hdl.handle.net/11452/29532 | - |
dc.description.abstract | This study presents the application of seven recent meta-heuristic optimization algorithms to automate design of disk cam mechanism with translating roller follower regarding four follower motion laws. The algorithms are: salp swarm algorithm (SSA), moth-flame optimization (MFO), ant lion optimizer (ALO), multi verse optimizer (MVO), grey wolf optimizer (GWO), evaporation rate water cycle algorithm (ER-WCA), and mine blast algorithm (MBA). The optimum cam design problem is formulated with three objectives including the minimum congestion, maximum performance, and maximum strength resistance of the cam. Moreover, the effect of selecting follower motion law on the optimal design of mechanism is investigated. The computational results clearly indicate that the utilized algorithms are very competitive in structural design optimization, especially MBA, ER-WCA, MFO and GWO techniques. Among the four follower motion laws, the polynomial 3-4-5 degree is the best one. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Metaheuristics | en_US |
dc.subject | Optimization | en_US |
dc.subject | Cam mechanism | en_US |
dc.subject | Follower motion law | en_US |
dc.subject | Grey wolf optimizer | en_US |
dc.subject | Salp swarm optimizer | en_US |
dc.subject | Moth-flame optimizer | en_US |
dc.subject | Multi verse optimizer | en_US |
dc.subject | Ant lion optimizer | en_US |
dc.subject | Water cycle algorithm | en_US |
dc.subject | Optimum design | en_US |
dc.subject | Profile optimization | en_US |
dc.subject | Residual vibrations | en_US |
dc.subject | Sliding velocity | en_US |
dc.subject | Grey wolf | en_US |
dc.subject | Ant lion | en_US |
dc.subject | Evolutionary | en_US |
dc.subject | Search | en_US |
dc.subject | Minimization | en_US |
dc.subject | Cams | en_US |
dc.subject | Electric resistance | en_US |
dc.subject | Heuristic algorithms | en_US |
dc.subject | Optimization | en_US |
dc.subject | Structural design | en_US |
dc.subject | Cam mechanism | en_US |
dc.subject | Meta heuristics | en_US |
dc.subject | Motion law | en_US |
dc.subject | Optimizers | en_US |
dc.subject | Salp swarms | en_US |
dc.subject | Structural optimization | en_US |
dc.title | Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism | en_US |
dc.type | Article | en_US |
dc.identifier.wos | 000517663200023 | tr_TR |
dc.identifier.scopus | 2-s2.0-85075886268 | tr_TR |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi | tr_TR |
dc.contributor.department | Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü. | tr_TR |
dc.contributor.orcid | 0000-0003-1790-6987 | tr_TR |
dc.identifier.volume | 191 | tr_TR |
dc.relation.journal | Knowledge-Based Systems | en_US |
dc.contributor.buuauthor | Yıldız, Ali Rıza | - |
dc.relation.collaboration | Yurt dışı | tr_TR |
dc.subject.wos | Computer science | en_US |
dc.subject.wos | Artificial intelligence | en_US |
dc.indexed.wos | SCIE | en_US |
dc.indexed.scopus | Scopus | en_US |
dc.wos.quartile | Q1 | en_US |
dc.contributor.scopusid | 7102365439 | tr_TR |
dc.subject.scopus | Cams; Follower; Biomechanics | en_US |
Appears in Collections: | Scopus Web of Science |
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