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  • Truss optimization with natural frequency constraints using generalized normal distribution optimization

    Author(s)
    Khodadadi, N
    Mirjalili, S
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2022
    Metadata
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    Abstract
    The newly proposed Generalized Normal Distribution Optimization (GNDO) algorithm is used to design the truss structures with optimal weight. All trusses optimized have frequency constraints, which make them very challenging optimization problems. A large number of locally optimal solutions and non-convexity of search space make them difficult to solve, therefore, they are suitable for testing the performance of optimization algorithm. This work investigates whether the proposed algorithm is capable of coping with such problems. To evaluate the GNDO algorithm, three benchmark truss optimization problems are considered with ...
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    The newly proposed Generalized Normal Distribution Optimization (GNDO) algorithm is used to design the truss structures with optimal weight. All trusses optimized have frequency constraints, which make them very challenging optimization problems. A large number of locally optimal solutions and non-convexity of search space make them difficult to solve, therefore, they are suitable for testing the performance of optimization algorithm. This work investigates whether the proposed algorithm is capable of coping with such problems. To evaluate the GNDO algorithm, three benchmark truss optimization problems are considered with frequency constraints. Numerical data show GNDO’s reliability, stability, and efficiency for structural optimization problems than other meta-heuristic algorithms. We thoroughly analyse and investigate the performance of GNDO in this engineering area for the first time in the literature.
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    Journal Title
    Applied Intelligence
    DOI
    https://doi.org/10.1007/s10489-021-03051-5
    Note
    This publication has been entered as an advanced online version in Griffith Research Online.
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/411874
    Collection
    • Journal articles

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