Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/29532
Title: Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism
Authors: Abderazek, Hammoudi
Mirjalili, Seyedali
Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.
0000-0003-1790-6987
Yıldız, Ali Rıza
7102365439
Keywords: 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
Issue Date: 12-Nov-2019
Publisher: Elsevier
Citation: Abderazek, H. vd. (2020). "Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism". Knowledge-Based Systems, 191.
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.
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
Appears in Collections:Scopus
Web of Science

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