بدائل البحث:
algorithm machine » algorithm achieves (توسيع البحث), algorithm within (توسيع البحث)
machine function » achieve functions (توسيع البحث), sine function (توسيع البحث)
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث)
algorithm both » algorithm blood (توسيع البحث), algorithm b (توسيع البحث), algorithm etc (توسيع البحث)
algorithm machine » algorithm achieves (توسيع البحث), algorithm within (توسيع البحث)
machine function » achieve functions (توسيع البحث), sine function (توسيع البحث)
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث)
algorithm both » algorithm blood (توسيع البحث), algorithm b (توسيع البحث), algorithm etc (توسيع البحث)
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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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Cost function calculated by QA with different hyperparameters.
منشور في 2025الموضوعات: "…currently available algorithms…"
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Cost function calculated by SA with different hyperparameters.
منشور في 2025الموضوعات: "…currently available algorithms…"
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Comparison of different algorithms.
منشور في 2025"…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …"
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