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http://hdl.handle.net/11452/29532
Başlık: | Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism |
Yazarlar: | Abderazek, Hammoudi Mirjalili, Seyedali Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü. 0000-0003-1790-6987 Yıldız, Ali Rıza 7102365439 |
Anahtar kelimeler: | Algorithms Metaheuristics Optimization Cam mechanism Follower motion law Grey wolf optimizer Salp swarm optimizer Moth-flame optimizer Multi verse optimizer Ant lion optimizer Water cycle algorithm Optimum design Profile optimization Residual vibrations Sliding velocity Grey wolf Ant lion Evolutionary Search Minimization Cams Electric resistance Heuristic algorithms Optimization Structural design Cam mechanism Meta heuristics Motion law Optimizers Salp swarms Structural optimization |
Yayın Tarihi: | 12-Kas-2019 |
Yayıncı: | Elsevier |
Atıf: | Abderazek, H. vd. (2020). "Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism". Knowledge-Based Systems, 191. |
Özet: | 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. |
URI: | https://doi.org/10.1016/j.knosys.2019.105237 https://www.sciencedirect.com/science/article/pii/S0950705119305568 http://hdl.handle.net/11452/29532 |
ISSN: | 0950-7051 |
Koleksiyonlarda Görünür: | Scopus Web of Science |
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