Showing 1 - 20 results of 66 for search '(( binary data structure optimization algorithm ) OR ( lines from global optimization algorithm ))', query time: 0.73s Refine Results
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    Algorithm parameters. by Ruyi Dong (9038174)

    Published 2025
    “…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”
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    Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures by Hongpeng He (348094)

    Published 2021
    “…Both in silico design and data analysis descriptors and norm index were employed to encode the structural characteristics of binary IL mixtures. …”
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    Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization by Sandeep Jagonda Patil (22048337)

    Published 2025
    “…The performance of the proposed LEGAN-BEPO-BCMANET technique attains 29.786%, 19.25%, 22.93%, 27.21%, 31.02%, 26.91%, and 25.61% greater throughput, compared to existing methods like Blockchain-based BATMAN protocol utilizing MANET with an ensemble algorithm (BATMAN-MANET), Block chain-based trusted distributed routing scheme with optimized dropout ensemble extreme learning neural network in MANET (DEELNN-MANET), A secured trusted routing utilizing structure of a new directed acyclic graph-blockchain in MANET internet of things environment (DAG-MANET), An Optimized Link State Routing Protocol with Blockchain Framework for Efficient Video-Packet Transmission and Security over MANET (OLSRP-MANET), Auto-metric Graph Neural Network based Blockchain Technology for Protected Dynamic Optimum Routing in MANET (AGNN-MANET) and Data security-based routing in MANETs under key management process (DSR-MANET) respectively.…”
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    Test function results. by Ruyi Dong (9038174)

    Published 2025
    “…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”
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    The set of Pareto solutions. by Ruyi Dong (9038174)

    Published 2025
    “…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”
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    Flowchart of LLSKSO. by Ruyi Dong (9038174)

    Published 2025
    “…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”
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    S1 Raw images. by Ruyi Dong (9038174)

    Published 2025
    “…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”
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    Flow chart of carbon fiber production. by Ruyi Dong (9038174)

    Published 2025
    “…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”
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    Benchmark test function results. by Ruyi Dong (9038174)

    Published 2025
    “…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”
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    Benchmark test functions. by Ruyi Dong (9038174)

    Published 2025
    “…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”
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    High-dimensional benchmark test functions. by Ruyi Dong (9038174)

    Published 2025
    “…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”
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