Heuristic genetic algorithm parameter optimizer: Making lossless compression algorithms efficient and flexible

Weijie Wang, Wenhui Chen, Li Yan, Yanqing Yang, Huihuang Zhao

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.