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Showing 1 - 20 results of 16,942 for search '(((( algorithm time function ) OR ( algorithm rate function ))) OR ( algorithm a function ))', query time: 0.93s Refine Results
  1. 1
    Run times of two algorithms.

    Run times of two algorithms. by Gourab Saha (8987405)

    Published 2025
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  2. 2
    Completion times for different algorithms.

    Completion times for different algorithms. by Jianbin Zheng (587000)

    Published 2025
    “…Additionally, value function normalization and adaptive learning rate strategies are applied to accelerate convergence. …”
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  3. 3
    Artificial colony algorithm flow.

    Artificial colony algorithm flow. by Jianpeng Zhang (528185)

    Published 2024
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  4. 4
    Pseudo-code of ABC algorithm.

    Pseudo-code of ABC algorithm. by Jianpeng Zhang (528185)

    Published 2024
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  5. 5
    Qualitative results for the F12 function.

    Qualitative results for the F12 function. by Dingming Yang (8505990)

    Published 2022
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  6. 6
    Qualitative results for the F14 function.

    Qualitative results for the F14 function. by Dingming Yang (8505990)

    Published 2022
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  7. 7
    Qualitative results for the F15 function.

    Qualitative results for the F15 function. by Dingming Yang (8505990)

    Published 2022
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  8. 8
    Qualitative results for the F13 function.

    Qualitative results for the F13 function. by Dingming Yang (8505990)

    Published 2022
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  9. 9
    Parameters of the test function.

    Parameters of the test function. by Yiqiong Gao (16698968)

    Published 2023
    “…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”
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  10. 10
    Optimization results of different algorithms.

    Optimization results of different algorithms. by Haijian Wang (746739)

    Published 2024
    “…<div><p>An Aquila optimizer-back propagation (AO-BP) neural network was used to establish an approximate model of the relationship between the design variables and the optimization objective to improve elevator block brake capabilities and achieve a lightweight brake design. Subsequently, the constraint conditions and objective functions were determined. …”
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  11. 11
    (a-c) Sampling event times with an intensity function .

    (a-c) Sampling event times with an intensity function . by Gerrit Großmann (9585147)

    Published 2020
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  12. 12
    Convergence curve of each DSM algorithms.

    Convergence curve of each DSM algorithms. by Manoj Kumar V. (18196645)

    Published 2024
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  13. 13
    Optimization flow chart of the AO algorithm.

    Optimization flow chart of the AO algorithm. by Haijian Wang (746739)

    Published 2024
    “…<div><p>An Aquila optimizer-back propagation (AO-BP) neural network was used to establish an approximate model of the relationship between the design variables and the optimization objective to improve elevator block brake capabilities and achieve a lightweight brake design. Subsequently, the constraint conditions and objective functions were determined. …”
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  14. 14
    WERs for different SE algorithms.

    WERs for different SE algorithms. by Fahad Khalil Peracha (15453204)

    Published 2023
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  15. 15
    Cleavage time as a function of scaled fiber diameter.

    Cleavage time as a function of scaled fiber diameter. by Roukayatou R. Ouedraogo (20460980)

    Published 2024
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  16. 16
    The optimal contact/quarantine rates from the family of functions (4) and (5) for Hainan and Xinjiang.

    The optimal contact/quarantine rates from the family of functions (4) and (5) for Hainan and Xinjiang. by Mengqi He (5753525)

    Published 2023
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  17. 17
    The optimal contact/quarantine rates from the family of functions (4) and (5) for Xi’an, Guangzhou and Yangzhou.

    The optimal contact/quarantine rates from the family of functions (4) and (5) for Xi’an, Guangzhou and Yangzhou. by Mengqi He (5753525)

    Published 2023
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  18. 18
    Average CPU time (s) of all the referenced algorithm on benchmark function.

    Average CPU time (s) of all the referenced algorithm on benchmark function. by Aihua Guo (13267712)

    Published 2022
    “…<p>Average CPU time (s) of all the referenced algorithm on benchmark function.…”
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  19. 19
    The results of each test function.

    The results of each test function. by Yuhang Wang (332111)

    Published 2025
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  20. 20
    The average cumulative reward of algorithms.

    The average cumulative reward of algorithms. by Jianbin Zheng (587000)

    Published 2025
    “…Additionally, value function normalization and adaptive learning rate strategies are applied to accelerate convergence. …”
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