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Showing 1 - 20 results of 97 for search '(( binary mask driven optimization algorithm ) OR ( level ranked based optimization algorithm ))', query time: 0.44s 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
    The average ranks.

    The average ranks. by Jiaqi Ma (773451)

    Published 2024
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  3. 3
    Wilcoxon rank-sum test.

    Wilcoxon rank-sum test. by Ying Li (38224)

    Published 2022
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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
    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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  7. 7
    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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  8. 8
    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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  9. 9
    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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  10. 10
    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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  11. 11
    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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  12. 12
    Patronage pattern from the perspective of facility.

    Patronage pattern from the perspective of facility. 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>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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  14. 14
    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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  15. 15
    Performance comparison of (REA) and (RDOA-D).

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

    Published 2022
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  16. 16
    Patronage pattern from the perspective of customer.

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

    Published 2022
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  17. 17
    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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  18. 18
    Optimal traffic volume.

    Optimal traffic volume. by Yangjun Ren (17691912)

    Published 2024
    “…Based on the problem characteristics, a Migratory Birds Optimization Algorithm with a new product source encoding scheme is developed as a solution approach. …”
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  19. 19
    Bayesian Algorithm for Retrosynthesis

    Bayesian Algorithm for Retrosynthesis by Zhongliang Guo (300613)

    Published 2020
    “…Remarkably, the Monte Carlo algorithm, which was specifically designed for the presence of multiple diverse routes, often revealed a ranked list of hundreds of reaction routes to the same synthetic target. …”
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  20. 20
    Bayesian Algorithm for Retrosynthesis

    Bayesian Algorithm for Retrosynthesis by Zhongliang Guo (300613)

    Published 2020
    “…Remarkably, the Monte Carlo algorithm, which was specifically designed for the presence of multiple diverse routes, often revealed a ranked list of hundreds of reaction routes to the same synthetic target. …”
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