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Optimizing Gradient Methods for IoT Applications
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DOI：10.1109/JIOT.2022.3142200.png)
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Abstract
En 中文
Solving linear programming (LP) and nonlinear programming (NLP) problems is momentous because of their wide applications in real-life problems. There is no unified way to find the global optimum for NLPs. But on the other hand, the simplex algorithm, as the dominating methodology for LPs for several decades moves only on the boundary (vertices) and ignores the vast majority of the feasible region in the process of searching. In this article, we study two gradient-based methodologies that explore the whole feasible region, which guarantee faster convergence rates for both LP and NLP optimization problems including IoT problems such as Software-Defined Internet of Vehicles (SDIoV) and vehicular ad hoc networks (VANETs). The gradient-simplex algorithm (GSA) for LPs, which moves inside the feasible region in the gradient direction at first to reduce the search space and then explores the reduced boundary to find an optimal solution. The evolutionary-gradient algorithm (EGA), on the other hand, is for NLPs and uses an evolutionary population to estimate gradients by evolving to find better solutions in steps. Based on extensive simulations, the obtained numerical results show that both approaches provide efficient solutions and outperform the state-of-the-art methods on optimization problems with large feasible spaces. Comparative results of applying the GSA on SDIoV and VANETs with different sizes are included.
Keywords:
Statistics
Sociology
Internet of Things
Approximation algorithms
Software algorithms
Linear programming
Optimization
Evolutionary population
feasible region
gradient function
Internet of Things
simplex algorithm
Journal
IF:
8.9
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1.4W
Citations:
7.8W