An Adaptive Data Compression Technique Using the Horse Herd Optimization Algorithm for Smart Grid Data

The rapid growth of smart grid systems has resulted in an exponential increase in data volume. This has led to significant challenges for efficient storage and transmission, thereby necessitating the development of advanced data compression techniques. Traditional techniques often struggle to mainta...

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Veröffentlicht in:International journal of computational intelligence systems Jg. 18; H. 1; S. 258 - 31
Hauptverfasser: Selvakumar, Karthika, Ponpandi, Rathika
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 10.10.2025
Springer Nature B.V
Springer
Schlagworte:
Adaptive algorithms
Artificial Intelligence
Compression ratio
Computational Intelligence
Control
Data compression
Data integrity
Data management
Datasets
Decision making
Decomposition
Discrete Wavelet Transform
Effectiveness
Electricity
Engineering
Fourier transforms
Genetic algorithms
Horse herd optimization
Localization
Mathematical Logic and Foundations
Mechatronics
Multi-objective particle swarm optimization
Multiple objective analysis
Neural networks
Optimization
Optimization algorithms
Optimization techniques
Pareto optimum
Performance measurement
Real time
Robotics
Signal to noise ratio
Smart grid
Smart grid data
Smart meters
Wavelet transform
Wavelet transforms
Multi-objective particle swarm optimization
Smart grid data
Wavelet transform
Data compression
Horse herd optimization
ISSN:1875-6883, 1875-6891, 1875-6883
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Zusammenfassung:The rapid growth of smart grid systems has resulted in an exponential increase in data volume. This has led to significant challenges for efficient storage and transmission, thereby necessitating the development of advanced data compression techniques. Traditional techniques often struggle to maintain a balance between compression efficiency and data integrity, particularly when dealing with diverse and large datasets. To address this issue, this paper presents an adaptive data compression algorithm based on discrete wavelet transform (DWT) and Horse Herd Optimization (HHO). The proposed technique significantly improves storage by dynamically optimizing key performance metrics, such as signal-to-noise ratio (SNR), mean square error (MSE), and compression ratio (CR). By employing a robust optimization algorithm, it effectively addresses the computational challenges of processing large-scale and real-time data. This adaptability ensures the algorithm is well-suited for dynamic smart grid environments, providing a scalable and reliable solution to modern data management demands. This technique uses HHO to find the optimal thresholding for smart grid data compression. In general, DWT-based data compression is carried out using a universal threshold for ignoring particular wavelet coefficients. But, the performance of data compression varies for different threshold values. Hence, selecting an optimal threshold is a challenging task for data compression. Therefore, to solve this issue, an effective optimization algorithm is needed. In this work, a Multi-Objective Horse Herd Optimization (MO-HHO) algorithm is proposed to find the optimal threshold. The suggested MO-HHO algorithm accurately determines the global optimum threshold. Hence, it maintains a good compromise between SNR, MSE and CR. The effectiveness of the proposed algorithm is examined using three different data sets. Various datasets from the IEEE Power quality wave data, Household Electric Power Consumption data and Real-time dataset from an experimental set-up were used to test the proposed algorithm. The outcome demonstrates that the suggested MO-HHO algorithm performs better than the other conventional methods.
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ISSN:1875-6883
1875-6891
1875-6883
DOI:10.1007/s44196-025-00999-x