Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2024, Volume and Issue: 14(6)

Published: Aug. 18, 2024

Abstract This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects QLMA, including parameter adaptation, operator selection, and balancing global exploration local exploitation. QLMA has become a leading solution industries like energy, power systems, engineering, addressing range mathematical challenges. Looking forward, we suggest further integration, transfer learning strategies, techniques to reduce state space. article is categorized under: Technologies > Computational Intelligence Artificial

Language: Английский

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Applications of deep learning techniques for predicting dynamic service location enhanced scheduling algorithm in foggy computing environment

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 117, P. 183 - 192

Published: Jan. 14, 2025

Language: Английский

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The applications of machine learning mechanisms in the compositions of internet of things services: A systematic study, current progress, and future research agenda

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 147, P. 110345 - 110345

Published: Feb. 26, 2025

Language: Английский

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Machine learning in industrialization: a bibliometric analysis

DYNA, Journal Year: 2025, Volume and Issue: 92(235), P. 28 - 37

Published: Feb. 3, 2025

Machine learning is currently emerging as one of the most rapidly advancing technologies, with a recent upward trend in its use for process automation across industrial processes. The objective this study was to conduct bibliometric analysis identify research trends machine learning. Scopus database used scientific production. Bibliometric indicators visibility, impact, and concurrence were analyzed. 7,335 documents, involving 22,383 authors, showed growth rate 20.86% from 1988 early 2024. Three dominant identified: first based on applications processes, second referring human factor artificial intelligence, third related convolutional neural networks.

Language: Английский

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Service Placement in Fog Computing Using a Combination of Reinforcement Learning and Improved Gray Wolf Optimization Method

Concurrency and Computation Practice and Experience, Journal Year: 2025, Volume and Issue: 37(9-11)

Published: April 24, 2025

ABSTRACT Fog computing extends cloud to the edge of network, bringing processing and storage capabilities closer end users Internet Things (IoT) devices. This paradigm helps reduce latency, improve response time, optimize bandwidth usage. In environment, service availability is a criterion for determining user satisfaction, which strongly influenced by time optimal allocation network resources (communication bandwidth). Service placement in fog refers process locations placing services network. this paper, done being aware volume requests from nodes using neural networks, reinforcement learning, improved gray wolf optimization (IGWO) method. Based on results obtained simulation, proposed approach has less (between 5% 21%), more favorable load balance, utility value (12%) lower Energy consumption minimum 10% maximum 25%.

Language: Английский

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DBSCAN based approach for energy efficient VM placement using medium level CPU utilization

Sustainable Computing Informatics and Systems, Journal Year: 2024, Volume and Issue: 43, P. 101025 - 101025

Published: Aug. 2, 2024

Language: Английский

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Evaluation of Optimization Algorithm for Application Placement Problem in Fog Computing: A Systematic Review

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 20, 2025

Language: Английский

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Optimization of Big Data Analysis Resources Supported by XGBoost Algorithm: Comprehensive Analysis of Industry 5.0 and ESG Performance

Measurement Sensors, Journal Year: 2024, Volume and Issue: unknown, P. 101310 - 101310

Published: Oct. 1, 2024

Language: Английский

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VM consolidation steps in cloud computing: A perspective review

Simulation Modelling Practice and Theory, Journal Year: 2024, Volume and Issue: 138, P. 103034 - 103034

Published: Nov. 9, 2024

Language: Английский

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Investigation of a high-performance control algorithm for a unified chaotic system synchronization control based on parameter adaptive method

Intelligent Decision Technologies, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 15

Published: July 4, 2024

With the rapid development of computer technology, parameter adaptive control methods are becoming more and widely used in nonlinear systems. However, there still many problems with synchronous controllers multiple inputs a single output, uncertainty, dynamic characteristics. This paper analyzed synchronization strategy uncoupled systems based on factors to adjust performance controller, briefly introduced manifestations chaotic motion. The characteristics differences continuous feedback transmission transfer were pointed out. Simple, effective, stable, feasible was using parameter-adaptive theory. By analyzing non-linear relationships between various models at different orders, fuzzy distribution second-order mean their independent uncorrelated matrices obtained, corresponding law formulas established solve functional expression state variables system. error risk test, computational complexity score chaos system effect test carried out algorithms traditional methods. Parameter found effectively reduce high-performance for unified calculation process simplified reduced by 0.6. application could improve algorithms, effectiveness rating improved. experimental results proved which greatly enriched field modern applications also drove vigorous dynamics research, thus making significant progress research.

Language: Английский

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