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  • Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism

    Author(s)
    Abderazek, Hammoudi
    Yildiz, Ali Riza
    Mirjalili, Seyedali
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2020
    Metadata
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    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 ...
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    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.
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    Journal Title
    Knowledge-Based Systems
    DOI
    https://doi.org/10.1016/j.knosys.2019.105237
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Psychology
    Artificial intelligence
    Data management and data science
    Machine learning
    Publication URI
    http://hdl.handle.net/10072/390850
    Collection
    • Journal articles

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