Green Communication for Sixth-Generation Intent-Based Networks: An Architecture Based on Hybrid Computational Intelligence Algorithm

The sixth-generation (6G) is envisioned as a pivotal technology that will support the ubiquitous seamless connectivity of substantial networks. The main advantage of 6G technology is leveraging Artificial Intelligence (AI) techniques for handling its interoperable functions. The pairing of 6G networks and AI creates new needs for infrastructure, data preparation, and governance. Thus, Intent-Based Network (IBN) architecture is a key infrastructure for 6G technology. Usually, these networks are formed of several clusters for data gathering from various heterogeneities in devices. Therefore, an important problem is to find the minimum transmission power for each node in the network clusters. This paper presents hybridization between two Computational Intelligence (CI) algorithms called the Marine Predator Algorithm and the Generalized Normal Distribution Optimization (MPGND). The proposed algorithm is applied to save power consumption which is an important problem in sustainable green 6G-IBN. MPGND is compared with several recently proposed algorithms, including Augmented Grey Wolf Optimizer (AGWO), Sine Tree-Seed Algorithm (STSA), Archimedes Optimization Algorithm (AOA), and Student Psychology-Based Optimization (SPBO). The experimental results with the statistical analysis demonstrate the merits and highly competitive performance of the proposed algorithm.

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An Improved GWO Algorithm Optimized RVFL Model for Oil Layer Prediction

Electronics ◽

10.3390/electronics10243178 ◽

2021 ◽

Vol 10 (24) ◽

pp. 3178

Author(s):

Pu Lan ◽

Kewen Xia ◽

Yongke Pan ◽

Shurui Fan

Keyword(s):

Chaos Theory ◽

Prediction Accuracy ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Practical Applications ◽

Marine Predator ◽

Search Speed ◽

Improved Grey Wolf Optimizer ◽

Better Than

In this study, a model based on the improved grey wolf optimizer (GWO) for optimizing RVFL is proposed to enable the problem of poor accuracy of Oil layer prediction due to the randomness of the parameters present in the random vector function link (RVFL) model to be addressed. Firstly, GWO is improved based on the advantages of chaos theory and the marine predator algorithm (MPA) to overcome the problem of low convergence accuracy in the optimization process of the GWO optimization algorithm. The improved GWO algorithm was then used to optimize the input weights and implicit layer biases of the RVFL network model so that the problem of inaccurate and unstable classification of RVFL due to the randomness of the parameters was avoided. MPA-GWO was used for comparison with algorithms of the same type under a function of 15 standard tests. From the results, it was concluded that it outperformed the algorithms of its type in terms of search accuracy and search speed. At the same time, the MPA-GWO-RVFL model was applied to the field of Oil layer prediction. From the comparison tests, it is concluded that the prediction accuracy of the MPA-GWO-RVFL model is on average 2.9%, 3.04%, 2.27%, 8.74%, 1.47% and 10.41% better than that of the MPA-RVFL, GWO-RVFL, PSO-RVFL, WOA-RVFL, GWFOA-RVFL and RVFL algorithms, respectively, and its practical applications are significant.

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An Application of Grey Wolf Optimizer for Solving Combined Economic Emission Dispatch Problems

International Review on Modelling and Simulations (IREMOS) ◽

10.15866/iremos.v7i5.2799 ◽

2014 ◽

Vol 7 (5) ◽

pp. 838 ◽

Cited By ~ 20

Author(s):

Hong Mee Song ◽

Mohd Herwan Sulaiman ◽

Mohd Rusllim Mohamed

Keyword(s):

Grey Wolf Optimizer ◽

Grey Wolf ◽

Emission Dispatch ◽

Economic Emission Dispatch

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Decision letter for "Electronically coupled photovoltaic system with grey wolf optimizer enabled DC ‐link voltage control loop"

10.1002/2050-7038.12648/v2/decision1 ◽

2020 ◽

Keyword(s):

Voltage Control ◽

Photovoltaic System ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Control Loop

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Design and Optimization of Fractional Order PIλ Dµ Controller Using Grey Wolf Optimizer for Automatic Voltage Regulator System

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096511666180124150326 ◽

2018 ◽

Vol 11 (2) ◽

pp. 217-226

Author(s):

Santosh Kumar Verma ◽

Shyam Krishna Nagar

Keyword(s):

Fractional Order ◽

Voltage Regulator ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Design And Optimization ◽

Automatic Voltage Regulator

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Optimal Sizing of an Island Hybrid Microgrid Based on Improved Multi-Objective Grey Wolf Optimizer

Processes ◽

10.3390/pr8121581 ◽

2020 ◽

Vol 8 (12) ◽

pp. 1581

Author(s):

Wenqiang Zhu ◽

Jiang Guo ◽

Guo Zhao ◽

Bing Zeng

Keyword(s):

Power Supply ◽

Tidal Current ◽

System Optimization ◽

Energy System ◽

Energy Sources ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Hybrid Energy System ◽

Hybrid Energy ◽

Multi Objective

The hybrid renewable energy system is a promising and significant technology for clean and sustainable island power supply. Among the abundant ocean energy sources, tidal current energy appears to be very valuable due to its excellent predictability and stability, particularly compared with the intermittent wind and solar energy. In this paper, an island hybrid energy microgrid composed of photovoltaic, wind, tidal current, battery and diesel is constructed according to the actual energy sources. A sizing optimization method based on improved multi-objective grey wolf optimizer (IMOGWO) is presented to optimize the hybrid energy system. The proposed method is applied to determine the optimal system size, which is a multi-objective problem including the minimization of annualized cost of system (CACS) and deficiency of power supply probability (DPSP). MATLAB software is utilized to program and simulate the hybrid energy system. Optimization results confirm that IMOGWO is feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. Furthermore, comparison of hybrid systems with and without tidal current turbines is undertaken to confirm that the utilization of tidal current turbines can contribute to enhancing system reliability and reducing system investment, especially in areas with abundant tidal energy sources.

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