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Showing 1 - 20 results of 222 for search '(( level based robust optimization algorithm ) OR ( binary based field optimization algorithm ))', query time: 0.70s Refine Results
  1. 1
    Optimality of the algorithm (REA).

    Optimality of the algorithm (REA). by Wuyang Yu (1644781)

    Published 2022
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  2. 2
    Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.XLSX

    Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.XLSX by Yangyang Liu (807797)

    Published 2022
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  3. 3
    Robustness test results of distribution system in province A of China.

    Robustness test results of distribution system in province A of China. by Qingbo Tu (22641033)

    Published 2025
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  4. 4
    Flowchart of the algorithm (REA).

    Flowchart of the algorithm (REA). by Wuyang Yu (1644781)

    Published 2022
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  5. 5
    Comparison of algorithm efficiency for solving (GCKP) (time unit: Second).

    Comparison of algorithm efficiency for solving (GCKP) (time unit: Second). by Wuyang Yu (1644781)

    Published 2022
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  6. 6
    Accuracy and average dots covered for difficulty level 11 using gradient descent to find the optimal solution.

    Accuracy and average dots covered for difficulty level 11 using gradient descent to find the optimal solution. by Tarun Khajuria (12117309)

    Published 2025
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  7. 7
    Optimal fitting model of acoustic pressure-rock physics parameters.

    Optimal fitting model of acoustic pressure-rock physics parameters. by Wei Jiang (14986)

    Published 2025
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  8. 8
    Image_1_Identification of a Novel Prognostic Signature for Gastric Cancer Based on Multiple Level Integration and Global Network Optimization.TIF

    Image_1_Identification of a Novel Prognostic Signature for Gastric Cancer Based on Multiple Level Integration and Global Network Optimization.TIF by Lin Cui (163750)

    Published 2021
    “…To mine for novel prognostic signatures associated with GC, we performed topological analysis, a random walk with restart algorithm, in the GCsLMM from three levels, miRNA-, mRNA-, and lncRNA-levels. …”
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  9. 9
    The location scheme for different <i>G</i> (<i>R</i><sub><i>c</i></sub> = 40, <i>R</i><sub><i>q</i></sub> = 80, <i>δ</i> = 100, <i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6).

    The location scheme for different <i>G</i> (<i>R</i><sub><i>c</i></sub> = 40, <i>R</i><sub><i>q</i></sub> = 80, <i>δ</i> = 100, <i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0... by Wuyang Yu (1644781)

    Published 2022
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  10. 10
    The relationship between market share and <i>R</i><sub><i>q</i></sub> (<i>R</i><sub><i>c</i></sub> = 40, <i>δ</i> = 100, <i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6).

    The relationship between market share and <i>R</i><sub><i>q</i></sub> (<i>R</i><sub><i>c</i></sub> = 40, <i>δ</i> = 100, <i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6). by Wuyang Yu (1644781)

    Published 2022
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  11. 11
    The relationship between market share and <i>R</i><sub><i>c</i></sub> (<i>R</i><sub><i>q</i></sub> = 80, <i>δ</i> = 100, <i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6).

    The relationship between market share and <i>R</i><sub><i>c</i></sub> (<i>R</i><sub><i>q</i></sub> = 80, <i>δ</i> = 100, <i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6). by Wuyang Yu (1644781)

    Published 2022
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  12. 12
    Location scheme of the new entrant (<i>γ</i><sub>1</sub> = 0.9, <i>γ</i><sub>2</sub> = 1).

    Location scheme of the new entrant (<i>γ</i><sub>1</sub> = 0.9, <i>γ</i><sub>2</sub> = 1). by Wuyang Yu (1644781)

    Published 2022
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  13. 13
    The relationship between market share and <i>δ</i> (<i>R</i><sub><i>q</i></sub> = 80, <i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6).

    The relationship between market share and <i>δ</i> (<i>R</i><sub><i>q</i></sub> = 80, <i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6). by Wuyang Yu (1644781)

    Published 2022
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  14. 14
    The location scheme for different <i>δ</i> (<i>R</i><sub><i>c</i></sub> = 40, <i>R</i><sub><i>q</i></sub> = 80, <i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6).

    The location scheme for different <i>δ</i> (<i>R</i><sub><i>c</i></sub> = 40, <i>R</i><sub><i>q</i></sub> = 80, <i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6). by Wuyang Yu (1644781)

    Published 2022
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  15. 15
    Patronage pattern from the perspective of facility.

    Patronage pattern from the perspective of facility. by Wuyang Yu (1644781)

    Published 2022
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  16. 16
    The relationship between market share and <i>δ</i> (<i>R</i><sub><i>c</i></sub> = 40, <i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6).

    The relationship between market share and <i>δ</i> (<i>R</i><sub><i>c</i></sub> = 40, <i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6). by Wuyang Yu (1644781)

    Published 2022
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  17. 17
    Location scheme of the new entrant (<i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6).

    Location scheme of the new entrant (<i>γ</i><sub>1</sub> = 0.4, <i>γ</i><sub>2</sub> = 0.6). by Wuyang Yu (1644781)

    Published 2022
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  18. 18
    Performance comparison of (REA) and (RDOA-D).

    Performance comparison of (REA) and (RDOA-D). by Wuyang Yu (1644781)

    Published 2022
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  19. 19
    Patronage pattern from the perspective of customer.

    Patronage pattern from the perspective of customer. by Wuyang Yu (1644781)

    Published 2022
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  20. 20
    The data set of the 49-nodes example in subsection 6.2 is provided as S1 File.

    The data set of the 49-nodes example in subsection 6.2 is provided as S1 File. by Wuyang Yu (1644781)

    Published 2022
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