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  • A Comparative Study of Recent Non-traditional Methods for Mechanical Design Optimization

    Author(s)
    Yildiz, AR
    Abderazek, H
    Mirjalili, S
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2019
    Metadata
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    Abstract
    Solving practical mechanical problems is considered as a real challenge for evaluating the efficiency of newly developed algorithms. The present article introduces a comparative study on the application of ten recent meta-heuristic approaches to optimize the design of six mechanical engineering optimization problems. The algorithms are: the artificial bee colony (ABC), particle swarm optimization (PSO) algorithm, moth-flame optimization (MFO), ant lion optimizer (ALO), water cycle algorithm (WCA), evaporation rate WCA (ER-WCA), grey wolf optimizer (GWO), mine blast algorithm (MBA), whale optimization algorithm (WOA) and salp ...
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    Solving practical mechanical problems is considered as a real challenge for evaluating the efficiency of newly developed algorithms. The present article introduces a comparative study on the application of ten recent meta-heuristic approaches to optimize the design of six mechanical engineering optimization problems. The algorithms are: the artificial bee colony (ABC), particle swarm optimization (PSO) algorithm, moth-flame optimization (MFO), ant lion optimizer (ALO), water cycle algorithm (WCA), evaporation rate WCA (ER-WCA), grey wolf optimizer (GWO), mine blast algorithm (MBA), whale optimization algorithm (WOA) and salp swarm algorithm (SSA). The performances of the algorithms are tested quantitatively and qualitatively using convergence speed, solution quality, and the robustness. The experimental results on the six mechanical problems demonstrate the efficiency and the ability of the algorithms used in this article.
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    Journal Title
    Archives of Computational Methods in Engineering
    DOI
    https://doi.org/10.1007/s11831-019-09343-x
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Mathematical Sciences
    Information and Computing Sciences
    Engineering
    Publication URI
    http://hdl.handle.net/10072/386763
    Collection
    • Journal articles

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