بدائل البحث:
algorithm both » algorithm blood (توسيع البحث), algorithm b (توسيع البحث), algorithm etc (توسيع البحث)
both function » body function (توسيع البحث), growth function (توسيع البحث), beach function (توسيع البحث)
algorithm spc » algorithm etc (توسيع البحث), algorithm pca (توسيع البحث), algorithm seu (توسيع البحث)
spc function » gpcr function (توسيع البحث), fc function (توسيع البحث), a function (توسيع البحث)
algorithm both » algorithm blood (توسيع البحث), algorithm b (توسيع البحث), algorithm etc (توسيع البحث)
both function » body function (توسيع البحث), growth function (توسيع البحث), beach function (توسيع البحث)
algorithm spc » algorithm etc (توسيع البحث), algorithm pca (توسيع البحث), algorithm seu (توسيع البحث)
spc function » gpcr function (توسيع البحث), fc function (توسيع البحث), a function (توسيع البحث)
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Continuous Probability Distributions generated by the PIPE Algorithm
منشور في 2022"…<div><p>Abstract We investigate the use of the Probabilistic Incremental Programming Evolution (PIPE) algorithm as a tool to construct continuous cumulative distribution functions to model given data sets. …"
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Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional
منشور في 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.
منشور في 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.
منشور في 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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Prediction performance of different optimization algorithms.
منشور في 2021"…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …"
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Algorithm of the brightness scale calibration experiment.
منشور في 2024"…<p>In the algorithm, the following variables were used: “I” denotes the current luminous intensity of the reference diode, “inc” denotes the current difference between reference and target diode luminous intensity; “cnt” is the current number of performed trials, while “correct” is a counter of correct answers in cnt trials, both of them are counted separately for every settings of I and inc. …"
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Scheduling time of five algorithms.
منشور في 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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Convergence speed of five algorithms.
منشور في 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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