بدائل البحث:
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث), algorithm both (توسيع البحث)
python function » protein function (توسيع البحث)
algorithm from » algorithm flow (توسيع البحث)
from function » from functional (توسيع البحث), fc function (توسيع البحث)
algorithm a » algorithms a (توسيع البحث), algorithm _ (توسيع البحث), algorithm b (توسيع البحث)
a function » _ function (توسيع البحث)
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث), algorithm both (توسيع البحث)
python function » protein function (توسيع البحث)
algorithm from » algorithm flow (توسيع البحث)
from function » from functional (توسيع البحث), fc function (توسيع البحث)
algorithm a » algorithms a (توسيع البحث), algorithm _ (توسيع البحث), algorithm b (توسيع البحث)
a function » _ function (توسيع البحث)
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Test function results.
منشور في 2025"…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …"
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Benchmark test functions.
منشور في 2025"…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …"
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Algorithm parameters.
منشور في 2025"…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …"
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CEC2017 basic functions.
منشور في 2025"…<div><p>To address the limitations of the Zebra Optimization Algorithm (ZOA), including insufficient late-stage optimization search capability, susceptibility to local optima, slow convergence, and inadequate exploration, this paper proposes an enhanced Zebra Optimization Algorithm integrating opposition-based learning and a dynamic elite-pooling strategy (OP-ZOA: Opposition-Based Learning Dynamic Elite-Pooling Zebra Optimization Algorithm). he proposed search algorithm employs a good point set-elite opposition-based learning mechanism to initialize the population, enhancing diversity and facilitating escape from local optima. …"
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DMTD algorithm.
منشور في 2025"…On the basis of EITO<sub>E</sub>, we propose EITO<sub>P</sub> algorithm using the PPO algorithm to optimize multiple objectives by designing reinforcement learning strategies, rewards, and value functions. …"
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Training algorithm flow.
منشور في 2024"…However, these statistical methods require collecting data from the entire research area, which consumes a significant amount of manpower and material resources. …"
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Imperialist competition algorithm with quasi-opposition-based learning for function optimization and engineering design problems
منشور في 2024"…The effectiveness of the proposed QOBL-ICA is verified by testing on 20 benchmark functions and 3 engineering design problems. Experimental results show that the performance of QOBL-ICA is superior to most state-of-the-art meta-heuristic algorithms in terms of global optimum reached and convergence speed.…"
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Control parameters of the SOMA algorithm.
منشور في 2025"…To optimize this cost function, we employ the self-organizing migrating algorithm, a swarm intelligence algorithm inspired by the cooperative and competitive behaviors observed in natural organisms. …"
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AUC scores of anomaly detection algorithms.
منشور في 2025"…This strategy is integrated into a random forest algorithm by replacing the conventional voting method. …"
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Recall scores of anomaly detection algorithms.
منشور في 2025"…This strategy is integrated into a random forest algorithm by replacing the conventional voting method. …"
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Benchmark test function results.
منشور في 2025"…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …"
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Flowchart of improved FOX optimization algorithm.
منشور في 2025"…Additionally, it achieved 880 wins, 228 ties, and 348 losses against 16 optimization algorithms across all involved functions and problems. …"
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A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density.
منشور في 2025"…<p>A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density.…"