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  • Grasshopper optimization algorithm for multi-objective optimization problems

    Author(s)
    Mirjalili, Seyedeh Zahra
    Mirjalili, Seyedali
    Saremi, Shahrzad
    Faris, Hossam
    Aljarah, Ibrahim
    Griffith University Author(s)
    Saremi, Shahrzad
    Mirjalili, Seyedali
    Year published
    2018
    Metadata
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    Abstract
    This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective ...
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    This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective test problems is utilized. The results are compared with the most well-regarded and recent algorithms in the literature of evolutionary multi-objective optimization using three performance indicators quantitatively and graphs qualitatively. The results show that the proposed algorithm is able to provide very competitive results in terms of accuracy of obtained Pareto optimal solutions and their distribution.
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    Journal Title
    Applied Intelligence
    Volume
    48
    Issue
    4
    DOI
    https://doi.org/10.1007/s10489-017-1019-8
    Subject
    Artificial intelligence
    Science & Technology
    Technology
    Computer Science, Artificial Intelligence
    Computer Science
    Optimization
    Publication URI
    http://hdl.handle.net/10072/406291
    Collection
    • Journal articles

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