Heuristic genetic algorithm parameter optimizer: Making lossless compression algorithms efficient and flexible
The processing of large volumes of time series data across various fields presents significant challenges, particularly when it comes to effectively managing floating-point numbers. Current dual precision floating-point lossless compression algorithms often struggle to deliver exceptional performanc...
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| Vydáno v: | Expert systems with applications Ročník 272; s. 126693 |
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| Jazyk: | angličtina |
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Elsevier Ltd
05.05.2025
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| ISSN: | 0957-4174 |
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| Abstract | The processing of large volumes of time series data across various fields presents significant challenges, particularly when it comes to effectively managing floating-point numbers. Current dual precision floating-point lossless compression algorithms often struggle to deliver exceptional performance on diverse datasets, highlighting their inherent limitations. To address this issue, we propose a novel method called the Heuristic Genetic Algorithm Parameter Optimizer for Lossless Compression of Time Series Floating Point Data (HGA-ACTF). This method features a highly effective parameter optimizer designed specifically for compression algorithms that utilize leading zeros. The combination of our parameter optimizer and the HGA-ACTF algorithm strategy has been proven to outperform existing leading compression algorithms across multiple fields. This approach not only enhances the compression ratio but also significantly reduces both compression and decompression times. In our comparative study, we evaluated the HGA-ACTF algorithm against eleven well-performing algorithms and a variant of the algorithm, integrating our parameter optimizer and algorithmic strategy into other adaptable algorithms, and demonstrating notable improvements. Experimental results indicate that the HGA-ACTF algorithm achieves an average compression ratio improvement of 38.87%, with some datasets showing improvements of up to 54.36%. Our approach effectively addresses the transmission and storage of time series data, significantly reducing the overhead associated with data processing. The code can be found at https://github.com/wwj10/HGA-ACTF. |
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| AbstractList | The processing of large volumes of time series data across various fields presents significant challenges, particularly when it comes to effectively managing floating-point numbers. Current dual precision floating-point lossless compression algorithms often struggle to deliver exceptional performance on diverse datasets, highlighting their inherent limitations. To address this issue, we propose a novel method called the Heuristic Genetic Algorithm Parameter Optimizer for Lossless Compression of Time Series Floating Point Data (HGA-ACTF). This method features a highly effective parameter optimizer designed specifically for compression algorithms that utilize leading zeros. The combination of our parameter optimizer and the HGA-ACTF algorithm strategy has been proven to outperform existing leading compression algorithms across multiple fields. This approach not only enhances the compression ratio but also significantly reduces both compression and decompression times. In our comparative study, we evaluated the HGA-ACTF algorithm against eleven well-performing algorithms and a variant of the algorithm, integrating our parameter optimizer and algorithmic strategy into other adaptable algorithms, and demonstrating notable improvements. Experimental results indicate that the HGA-ACTF algorithm achieves an average compression ratio improvement of 38.87%, with some datasets showing improvements of up to 54.36%. Our approach effectively addresses the transmission and storage of time series data, significantly reducing the overhead associated with data processing. The code can be found at https://github.com/wwj10/HGA-ACTF. |
| ArticleNumber | 126693 |
| Author | Yang, Yanqing Yan, Li Chen, Wenhui Zhao, Huihuang Wang, Weijie |
| Author_xml | – sequence: 1 givenname: Weijie orcidid: 0009-0001-7620-1859 surname: Wang fullname: Wang, Weijie organization: College of Computer Science and Technology, Hengyang Normal University, 421002, China – sequence: 2 givenname: Wenhui orcidid: 0000-0003-1437-5551 surname: Chen fullname: Chen, Wenhui email: whchen@hynu.edu.cn organization: College of Computer Science and Technology, Hengyang Normal University, 421002, China – sequence: 3 givenname: Li surname: Yan fullname: Yan, Li organization: College of Computer Science and Technology, Hengyang Normal University, 421002, China – sequence: 4 givenname: Yanqing surname: Yang fullname: Yang, Yanqing organization: College of Computer Science and Technology, Hengyang Normal University, 421002, China – sequence: 5 givenname: Huihuang surname: Zhao fullname: Zhao, Huihuang organization: College of Computer Science and Technology, Hengyang Normal University, 421002, China |
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| Cites_doi | 10.1007/s11047-022-09936-9 10.1007/s11042-020-10139-6 10.1016/j.cor.2023.106455 10.1016/j.neuroimage.2023.120331 10.1016/j.engappai.2023.106361 10.14778/3587136.3587149 10.1145/3582270 10.1145/3458817.3476224 10.1038/s42005-024-01558-0 10.14778/1687627.1687645 10.1007/s42235-023-00437-8 10.1016/j.eswa.2023.122147 10.1016/j.ymssp.2021.108709 10.1109/TII.2021.3110734 10.1016/j.segan.2023.101238 10.1016/j.compchemeng.2024.108657 10.1145/1014052.1014077 10.1016/j.ijhydene.2023.12.245 10.1016/j.compstruct.2023.117789 10.1007/978-3-319-93025-1_4 10.1109/IEEESTD.2019.8766229 10.1145/3584919 10.1145/3342555 10.1016/j.jksuci.2024.102246 10.1080/15397734.2021.1876569 10.14778/2824032.2824078 10.14778/3551793.3551852 10.1007/978-1-4471-7503-2_33 10.1038/s41598-024-58024-8 |
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| Keywords | Lossless compression Internet of things Heuristic genetic algorithm Time series floating point data Compression algorithm |
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