An improved human evolutionary optimization algorithm for maximizing green hydrogen generation in intelligent energy management system (IEMS)
This research presents an Intelligent Energy Management System (IEMS) based on the Internet of Things (IoT), fog, and cloud layers. IEMS is designed to optimize green hydrogen production at fog nodes within an IoT based on nanotechnology. The IEMS leverages an Improved Human Evolutionary Optimizatio...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025020705 |
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| Summary: | This research presents an Intelligent Energy Management System (IEMS) based on the Internet of Things (IoT), fog, and cloud layers. IEMS is designed to optimize green hydrogen production at fog nodes within an IoT based on nanotechnology. The IEMS leverages an Improved Human Evolutionary Optimization (IHEO) algorithm, based on the collected data at the fog's server, to effectively manage energy resources and maximize hydrogen generation while considering various constraints, such as renewable energy intermittency, energy demand fluctuations, and grid stability. The proposed IHEO algorithm incorporates novel mechanisms to enhance exploration and exploitation capabilities, leading to faster convergence and improved solution quality compared to traditional Human Evolutionary Optimization (HEO) and other recent algorithms. These algorithms include Sinh Cosh Optimization (SCHO), Chimp Optimization (Chimp), Improved Sine Cosine Algorithm (ISCA), Improved Grey Wolf Optimization (IGWO), and Coati Optimization Algorithm (COA). Simulation results proved that IHEO outperformed other optimization algorithms according to 5 benchmark functions. Additionally, results demonstrate the superior performance of the proposed IEMS in terms of maximizing hydrogen production, minimizing energy costs, and enhancing the overall efficiency and sustainability of the fog computing environment. According to the maximum iteration numbers of 100, 200, and 500 and the population's size of 500, 1000, and 1500, IHEO can provide the minimum cost (fitness) value, mean, and standard deviation at least execution time using KU-HMG Dataset. |
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| ISSN: | 2590-1230 |