Search alternatives:
algorithm blood » algorithm based (Expand Search), algorithm flow (Expand Search), algorithms based (Expand Search)
blood function » blood donation (Expand Search), based function (Expand Search), broad functional (Expand Search)
algorithm both » algorithm b (Expand Search), algorithm etc (Expand Search), algorithm co (Expand Search)
both function » body function (Expand Search), growth function (Expand Search), beach function (Expand Search)
algorithm blood » algorithm based (Expand Search), algorithm flow (Expand Search), algorithms based (Expand Search)
blood function » blood donation (Expand Search), based function (Expand Search), broad functional (Expand Search)
algorithm both » algorithm b (Expand Search), algorithm etc (Expand Search), algorithm co (Expand Search)
both function » body function (Expand Search), growth function (Expand Search), beach function (Expand Search)
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The ALO algorithm optimization flowchart.
Published 2024“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”
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The IALO algorithm solution flowchart.
Published 2024“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”
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Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional
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.
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.
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. …”