Search alternatives:
based optimization » whale optimization (Expand Search)
binary task » binary mask (Expand Search)
task based » risk based (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a based » ai based (Expand Search), _ based (Expand Search), 1 based (Expand Search)
based optimization » whale optimization (Expand Search)
binary task » binary mask (Expand Search)
task based » risk based (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a based » ai based (Expand Search), _ based (Expand Search), 1 based (Expand Search)
-
1
Proposed Algorithm.
Published 2025“…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
-
2
Comparisons between ADAM and NADAM optimizers.
Published 2025“…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
-
3
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. …”
-
4
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. …”
-
5
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. …”
-
6
An Example of a WPT-MEC Network.
Published 2025“…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
-
7
-
8
-
9
MSE for ILSTM algorithm in binary classification.
Published 2023“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
-
10
Related Work Summary.
Published 2025“…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
-
11
Simulation parameters.
Published 2025“…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
-
12
Training losses for N = 10.
Published 2025“…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
-
13
Normalized computation rate for N = 10.
Published 2025“…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
-
14
Summary of Notations Used in this paper.
Published 2025“…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
-
15
-
16
DE algorithm flow.
Published 2025“…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …”
-
17
Flowchart scheme of the ML-based model.
Published 2024“…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …”
-
18
Test results of different algorithms.
Published 2025“…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …”
-
19
-
20