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  • Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems

    Author(s)
    Gupta, Shubham
    Abderazek, Hammoudi
    Yildiz, Betul Sultan
    Yildiz, Ali Riza
    Mirjalili, Seyedali
    Sait, Sadiq M
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2021
    Metadata
    Show full item record
    Abstract
    Determining the solution for real mechanical design problems is a challenging task when using the newly developed and efficient swarm intelligence algorithms. There are so many difficulties to be addressed, including but not limited to mixed decision variables, diverse constraints, inherent errors, conflicting objectives, and numerous locally optimal solutions. This work analyzes the behavior of nine metaheuristic algorithms, namely, salp swarm algorithm (SSA), multi-verse optimizer (MVO), moth-flame optimizer (MFO), atom search optimization (ASO), ecogeography-based optimization (EBO), queuing search algorithm (QSA), ...
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    Determining the solution for real mechanical design problems is a challenging task when using the newly developed and efficient swarm intelligence algorithms. There are so many difficulties to be addressed, including but not limited to mixed decision variables, diverse constraints, inherent errors, conflicting objectives, and numerous locally optimal solutions. This work analyzes the behavior of nine metaheuristic algorithms, namely, salp swarm algorithm (SSA), multi-verse optimizer (MVO), moth-flame optimizer (MFO), atom search optimization (ASO), ecogeography-based optimization (EBO), queuing search algorithm (QSA), equilibrium optimizer (EO), evolutionary strategy (ES) and hybrid self-adaptive orthogonal genetic algorithm (HSOGA). The efficiency of these algorithms is evaluated on eight mechanical design problems using the solution quality and convergence analysis, which verifies the wide applicability of these algorithms to real-world application problems.
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    Journal Title
    Expert Systems with Applications
    Volume
    183
    DOI
    https://doi.org/10.1016/j.eswa.2021.115351
    Subject
    Mathematical sciences
    Applied computing
    Information and computing sciences
    Engineering
    Science & Technology
    Computer Science, Artificial Intelligence
    Engineering, Electrical & Electronic
    Operations Research & Management Science
    Publication URI
    http://hdl.handle.net/10072/407991
    Collection
    • Journal articles

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