بدائل البحث:
algorithm python » algorithms within (توسيع البحث), algorithm both (توسيع البحث)
within function » fibrin function (توسيع البحث), protein function (توسيع البحث), catenin function (توسيع البحث)
python function » protein function (توسيع البحث)
algorithm a » algorithms a (توسيع البحث), algorithm _ (توسيع البحث), algorithm b (توسيع البحث)
a function » _ function (توسيع البحث)
algorithm python » algorithms within (توسيع البحث), algorithm both (توسيع البحث)
within function » fibrin function (توسيع البحث), protein function (توسيع البحث), catenin function (توسيع البحث)
python function » protein 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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801
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802
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803
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804
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805
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806
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807
Metapopulation model notation.
منشور في 2025"…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …"
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808
Estimates of for each problem instance.
منشور في 2025"…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …"
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809
Approximation factors for each problem instance.
منشور في 2025"…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …"
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810
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811
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812
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813
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814
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815
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816
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817
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818
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819
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820
The structure of a speed reducer.
منشور في 2025"…In the experimental section, we validate the efficiency and superiority of LSWOA by comparing it with outstanding metaheuristic algorithms and excellent WOA variants. The experimental results show that LSWOA exhibits significant optimization performance on the benchmark functions with various dimensions. …"