Search alternatives:
based optimization » whale optimization (Expand Search)
primary data » primary care (Expand Search)
binary task » binary mask (Expand Search)
task based » risk based (Expand Search)
data swarm » data share (Expand Search)
based optimization » whale optimization (Expand Search)
primary data » primary care (Expand Search)
binary task » binary mask (Expand Search)
task based » risk based (Expand Search)
data swarm » data share (Expand Search)
-
1
-
2
-
3
-
4
-
5
-
6
-
7
-
8
-
9
Proposed Algorithm.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
-
10
-
11
S1 Data -
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
-
12
Curve of step response signal of 6 algorithms.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
-
13
Comparisons between ADAM and NADAM optimizers.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
-
14
The Pseudo-Code of the IRBMO Algorithm.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
-
15
-
16
IRBMO vs. meta-heuristic algorithms boxplot.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
-
17
IRBMO vs. feature selection algorithm boxplot.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
-
18
Wilcoxon’s rank sum test results.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
-
19
Flowchart of MSHHOTSA.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
-
20
Tension/compression spring design problem.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”