Showing 61 - 80 results of 1,987 for search '(( algorithm python function ) OR ((( algorithm both function ) OR ( algorithm b function ))))', query time: 0.57s Refine Results
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    Iteration curve of each algorithm: (a) Convergence curves of the average best fitness for functions F1-F10, (b) Convergence curves of the average best fitness for functions F11-F20 and (c) Correspondence between curve colors and algorithms. by Ruyi Dong (9038174)

    Published 2025
    “…<p>Iteration curve of each algorithm: (a) Convergence curves of the average best fitness for functions F1-F10, (b) Convergence curves of the average best fitness for functions F11-F20 and (c) Correspondence between curve colors and algorithms.…”
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    <b>Supplementary material for "Modified nonlocal strain gradient theory for static bending, free vibration and buckling analysis of functionally graded piezoelectric nanoplates"</b... by Phạm Văn Vinh (19543726)

    Published 2025
    “…Both nonlocal (softening) and strain gradient (hardening) effects are considered simultaneously in this novel theory. …”
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    Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

    Published 2025
    “…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …”
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    The pseudocode for the NAFPSO algorithm. by Huichao Guo (14515171)

    Published 2025
    “…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”
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    PSO algorithm flowchart. by Huichao Guo (14515171)

    Published 2025
    “…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”
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    Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large. by Panfei Li (22379086)

    Published 2025
    “…<p>Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large.…”
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