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    Shuffled Shepherd Optimization Method Simplified for Reducing the Parameter Dependency

    New search for: Kaveh, Ali
    New search for: Zaerreza, Ataollah

    Shuffled shepherd optimization algorithm (SSOA) is a newly developed multi-community algorithm inspired by shepherd behavior. Since the SSOA is a parameter-dependent method, the optimum performance requires using the right parameters for each problem. In this chapter, SSOA is modified to become less reliant on parameter adjustment.

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    Shuffled Shepherd Optimization Method Simplified for Reducing the Parameter Dependency

    New search for: Kaveh, Ali
    New search for: Zaerreza, Ataollah

    Shuffled shepherd optimization algorithm (SSOA) is a newly developed multi-community algorithm inspired by shepherd behavior. Since the SSOA is a parameter-dependent method, the optimum performance requires using the right parameters for each problem. In this chapter, SSOA is modified to become less reliant on parameter adjustment.

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Shuffled Shepherd Optimization Method Simplified for Reducing the Parameter Dependency

    New search for: Kaveh, Ali
    New search for: Zaerreza, Ataollah

    Shuffled shepherd optimization algorithm (SSOA) is a newly developed multi-community algorithm inspired by shepherd behavior. Since the SSOA is a parameter-dependent method, the optimum performance requires using the right parameters for each problem. In this chapter, SSOA is modified to become less reliant on parameter adjustment.

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    Check availability in my library
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    Title:

    Shuffled Shepherd Optimization Method Simplified for Reducing the Parameter Dependency

    Additional title:

    Studies in Systems, Decision and Control

    Contributors:

    Kaveh, Ali (author) / Zaerreza, Ataollah (author)

    Published in:

    Structural Optimization Using Shuffled Shepherd Meta-Heuristic Algorithm ; Chapter: 3 ; 53-76

    Studies in Systems, Decision and Control ; 463

    Publication date:

    2023-02-25

    Size:

    24 pages

    ISBN:

    978-3-031-25573-1 , 978-3-031-25572-4

    ISSN:

    2198-4190 , 2198-4182

    DOI:

    https://doi.org/10.1007/978-3-031-25573-1_3

    Type of media:

    Article/Chapter (Book)

    Type of material:

    Electronic Resource

    Language:

    English

    Keywords:

    Engineering , Civil Engineering , Manufacturing, Machines, Tools, Processes , Computational Intelligence

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