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Showing 141 - 160 results of 13,261 for search '(((( algorithm co function ) OR ( algorithm using functional ))) OR ( algorithm python function ))', query time: 0.80s Refine Results
  1. 141
    The material characteristics of the mass block.

    The material characteristics of the mass block. by Shangyang Jin (21772466)

    Published 2025
    “…<div><p>The convergence of algorithms is an unavoidable problem when using global optimization algorithms to optimize acoustic properties of metamaterials. …”
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  2. 142
    The process of the optimization (NSGA-Ⅱ).

    The process of the optimization (NSGA-Ⅱ). by Shangyang Jin (21772466)

    Published 2025
    “…<div><p>The convergence of algorithms is an unavoidable problem when using global optimization algorithms to optimize acoustic properties of metamaterials. …”
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  3. 143
    The process of the optimization (NSGA-Ⅱ+ NLPQL).

    The process of the optimization (NSGA-Ⅱ+ NLPQL). by Shangyang Jin (21772466)

    Published 2025
    “…<div><p>The convergence of algorithms is an unavoidable problem when using global optimization algorithms to optimize acoustic properties of metamaterials. …”
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  4. 144
    The TL of the metamaterial.

    The TL of the metamaterial. by Shangyang Jin (21772466)

    Published 2025
    “…<div><p>The convergence of algorithms is an unavoidable problem when using global optimization algorithms to optimize acoustic properties of metamaterials. …”
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  5. 145
    The nine structural parameters as design factors.

    The nine structural parameters as design factors. by Shangyang Jin (21772466)

    Published 2025
    “…<div><p>The convergence of algorithms is an unavoidable problem when using global optimization algorithms to optimize acoustic properties of metamaterials. …”
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  6. 146
    The material characteristics of the components.

    The material characteristics of the components. by Shangyang Jin (21772466)

    Published 2025
    “…<div><p>The convergence of algorithms is an unavoidable problem when using global optimization algorithms to optimize acoustic properties of metamaterials. …”
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  7. 147
    The process of the optimization (NSGA-Ⅱ).

    The process of the optimization (NSGA-Ⅱ). by Shangyang Jin (21772466)

    Published 2025
    “…<div><p>The convergence of algorithms is an unavoidable problem when using global optimization algorithms to optimize acoustic properties of metamaterials. …”
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  8. 148
    The TL of the perforated membrane metamaterial.

    The TL of the perforated membrane metamaterial. by Shangyang Jin (21772466)

    Published 2025
    “…<div><p>The convergence of algorithms is an unavoidable problem when using global optimization algorithms to optimize acoustic properties of metamaterials. …”
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  9. 149
    The process of the optimization (MIGA).

    The process of the optimization (MIGA). by Shangyang Jin (21772466)

    Published 2025
    “…<div><p>The convergence of algorithms is an unavoidable problem when using global optimization algorithms to optimize acoustic properties of metamaterials. …”
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  10. 150
    The effect of each parameter on the peak value.

    The effect of each parameter on the peak value. by Shangyang Jin (21772466)

    Published 2025
    “…<div><p>The convergence of algorithms is an unavoidable problem when using global optimization algorithms to optimize acoustic properties of metamaterials. …”
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  11. 151
    Schematic of CoCoNet for identifying trait relevant tissue/cell type using information from gene co-expression networks.

    Schematic of CoCoNet for identifying trait relevant tissue/cell type using information from gene co-expression networks. by Lulu Shang (8735736)

    Published 2020
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  12. 152
    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, the temporal molecular states <i>n</i> at specific time <i>t</i> will be generated for further visualization.

    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

    Published 2025
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  13. 153
    Eight commonly used benchmark functions.

    Eight commonly used benchmark functions. by Guangwei Liu (181992)

    Published 2023
    “…The proposed algorithm was evaluated on eight standard benchmark functions, CEC2019 benchmark functions, four engineering design problems, and a PID parameter optimization problem. …”
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  14. 154
    Benchmark functions T-test for the proposed and compared algorithms.

    Benchmark functions T-test for the proposed and compared algorithms. by El-Sayed M. El-kenawy (14581088)

    Published 2023
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  15. 155
    Proposed algorithm convergence curves versus other competing algorithms for the seven benchmark functions.

    Proposed algorithm convergence curves versus other competing algorithms for the seven benchmark functions. by El-Sayed M. El-kenawy (14581088)

    Published 2023
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  16. 156
    A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction methods.

    A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

    Published 2025
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  17. 157
    DataSheet_1_Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms.docx

    DataSheet_1_Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms.docx by Hang Zhou (230977)

    Published 2023
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  18. 158
    FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz

    FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz by Faizan Ali (21689861)

    Published 2025
    Subjects:
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  19. 159
    Graph of convergence of PSO algorithm fitness function.

    Graph of convergence of PSO algorithm fitness function. by Mohammed F. Elnaggar (18297954)

    Published 2024
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  20. 160
    Proposed algorithm mean and standard deviation (StDev) versus compared algorithms for the benchmark functions.

    Proposed algorithm mean and standard deviation (StDev) versus compared algorithms for the benchmark functions. by El-Sayed M. El-kenawy (14581088)

    Published 2023
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