A Hybrid Genetic Algorithm-Particle Swarm Optimization Approach for Enhanced Text Compression
DOI:
https://doi.org/10.21928/uhdjst.v8n2y2024.pp63-74Keywords:
Text Compression, Genetic Algorithms, Particle Swarm Optimization, Hybrid Algorithm, Data StorageAbstract
Text compression is a necessity for efficient data storage and transmission. Especially in the digital era, volumes of digital text have increased incredibly. Traditional text compression methods, including Huffman coding and Lempel-Ziv-Welch, have certain limitations regarding their adaptability and efficiency in dealing with such complexity and diversity of data. In this paper, we propose a hybrid method that combines Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) to optimize the compression of text using the broad exploration capabilities of GA and fast convergence properties of PSO. The experimental results reflect that the proposed hybrid approach of GA-PSO yields much better performance in compression ratio than the standalone methods by reducing the size to about 65% while retaining integrity in the original content. The proposed method is also highly adaptable to various text forms and outperformed other state-of-the-art methods such as the Grey Wolf Optimizer, the Whale Optimization Algorithm, and the African Vulture Optimization Algorithm. These results support that the hybrid method GA-PSO seems promising for modern text compression.
References
M. V. Mahoney. “Fast Text Compression with Neural Networks. In: Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2000)”. FLAIRS, 2000.
B. K. Kim, H. K. Song, T. Castells and S. Choi. “On Architectural Compression of Text-to-Image Diffusion Models. In Proceedings of the Neural Information Processing Systems (NeurIPS 2023). New Orleans, Louisiana, United States of America.
U. Manber. “A text compression scheme that allows fast searching directly in the compressed file”. ACM Transactions on Information Systems (TOIS), vol. 15, no. 2, pp. 124-136, 1997.
Z. Jiang, M. Yang and M. Tsirlin. “Low-resource” text classification: A parameter-free classification method with compressors”. In: Findings of the Association for Computational Linguistics: ACL, Stroudsburg, PA, 2023.
Y. Marton, N. Wu and L. Hellerstein. “On Compression-based Text Classification. In Advances in Information Retrieval: 27th European Conference on IR Research, ECIR 2005”. Springer, Santiago de Compostela, Spain, 2005.
P. Lewan and C. Khancome. “Bit-Level Affixation Text Compression Algorithms. In: 2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE)”. IEEE, 2024.
A. Moffat. “Huffman coding”. ACM Computing Surveys, vol. 52, no. 4, pp. 1-35, 2019.
H. N. Dheemanth. “LZW data compression”. American Journal of Engineering Research, vol. 3, no. 2, pp. 22-26, 2014.
E. P. Capo-Chichi, H. Guyennet and J. M. Friedt. “K-RLE: A New Data Compression Algorithm for Wireless Sensor Network. In 2009 Third International Conference on Sensor Technologies and Applications”. IEEE, 2009.
T. Li., T. Zhao, M. Nho and X. Zhou. “A Novel RLE & LZW for Bit-stream compression. In: 2016 13th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT)”. IEEE, 2016.
Y. Zhou, F. Zhang, T. Lin, Y. Huang, S. Long, J. Zhai and X. Du. “F-TADOC: FPGA-Based Text Analytics Directly on Compression with HLS. In: 2024 IEEE 40th International Conference on Data Engineering (ICDE)”. IEEE, 2024.
A. Moronfolu and D. Oluwade. “An enhanced LZW text compression algorithm”. The African Journal of Computing and ICT, vol. 2, no. 2, pp. 13-20, 2009.
F. Zhou, X. Huang, P. Zhang, M. Wang, Z. Wang, Y. Zhou and Y. I. N. Haibing. “Enhanced Screen Content Image Compression: A Synergistic Approach for Structural Fidelity and Text Integrity Preservation”. ACM Multimedia, New York, 2024.
S. Mirjalili. “Genetic algorithm”. In: Evolutionary Algorithms And Neural Networks: Theory and Applications. Springer, Cham, pp. 43-55, 2019.
J. Kennedy and R. Eberhart. “Particle Swarm Optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks”. IEEE, 1995.
V. Kachitvichyanukul. “Comparison of three evolutionary algorithms: GA, PSO, and DE”. Industrial Engineering and Management Systems, vol. 11, no. 3, pp. 215-223, 2012.
L. Haldurai, T. Madhubala and R. Rajalakshmi. “A study on genetic algorithm and its applications”. International Journal of Computer Sciences and Engineering, vol. 4, no. 10. pp. 139-143, 2016.
M. P. Song and G. C. Gu. “Research on Particle Swarm Optimization: A Review. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 04EX826)”. IEEE, 2004.
T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh and S. Mirjalili. “Particle swarm optimization: A comprehensive survey”. IEEE Access, vol. 10, pp. 10031-10061, 2022.
J. Zhou, R. Du, T. Yushan, J. Yang, Z. Yang, W. Luo, Z. Luo, X. Zhou and W. Hu. “Context Compression and Extraction: Efficiency Inference of Large Language Models. In: International Conference on Intelligent Computing”. Springer, 2024.
T. Bell, I. H. Witten and J. G. Cleary. “Modeling for text compression”. ACM Computing Surveys, vol. 21, no. 4, pp. 557-591, 1989.
A. Moffat. “Word-based text compression”. Software: Practice and Experience, vol. 19, no. 2, pp. 185-198, 1989.
I. H. Witten and T. C. Bell. “The zero-frequency problem: Estimating the probabilities of novel events in adaptive text compression”. IEEE Transactions on Information Theory, vol. 37, no. 4, pp. 1085-1094, 1991.
N. R. Brisaboa, A. Fariña, G. Navarro and J. R. Paramá. “Lightweight natural language text compression”. Information Retrieval Journal, vol. 10, pp. 1-33, 2007.
Z. Li, Z. Zhang, H. Zhao, R. Wang, K. Chen, M. Utiyama and E. Sumita. “Text compression-aided transformer encoding”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3840-3857, 2021.
P. Sarker and M. L. Rahman. “Introduction to Adjacent Distance Array with Huffman Principle: A New Encoding and Decoding Technique for Transliteration Based Bengali Text Compression. In Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2020”. Springer, 2021.
E. Priyono and H. Mustafidah. “Text compression using the Shannon-fano, Huffman, and half-byte algorithms”. International Journal of Scientific Research and Management, vol. 12, pp. 1422-1427, 2024.
H. Gilbert, M. Sandborn, D. C. Schmidt, J. Spencer-Smith and J. White. “Semantic Compression with Large Language Models. In: 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS)”. IEEE, 2023.
O. D. Adeniji, O. E. Akinola, A. O. Adesina and O. Afolabi. “Text Encryption with Advanced Encryption Standard (AES) for Near Field Communication (NFC) Using Huffman Compression. In: International Conference on Applied Informatics”. Springer, 2022.
J. R. Jayapandiyan, C. Kavitha and K. Sakthivel. “Optimal secret text compression technique for steganographic encoding by dynamic ranking algorithm”. Journal of Physics: Conference Series, vol. 1427, p. 012005.
H. Garg. “A hybrid PSO-GA algorithm for constrained optimization problems”. Applied Mathematics and Computation, vol 274, pp. 292-305, 2016.
Q. Zhang, R. M. Ogren and S. C. Kong. “A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO-GA and basic GA”. Applied Energy, vol. 165, pp. 676-684, 2016.
C. Li, R. Zhai. H. Liu, Y. Yang and H. Wu. “Optimization of a heliostat field layout using hybrid PSO-GA algorithm”. Applied Thermal Engineering, vol. 128. pp. 33-41, 2018.
M. Sheikhalishahi, V. Ebrahimipour, H. Shiri, H. Zaman and M. Jeihoonian. “A hybrid GA-PSO approach for reliability optimization in redundancy allocation problem”. The International Journal of Advanced Manufacturing Technology, vol. 68, pp. 317-338, 2013.
J. H. Holland. “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence”. MIT Press, United States, 1992.
S. Mirjalili and A. Lewis. “The whale optimization algorithm”. Advances in Engineering Software, vol. 95, pp. 51-67, 2016.
S. Mirjalili, S. M. Mirjalili and A. Lewis. “Grey wolf optimizer”. Advances in Engineering Software, vol. 69, pp. 46-61, 2014.
B. Abdollahzadeh, F. S. Gharehchopogh and S. Mirjalili. “African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems”. Computers & Industrial Engineering, vol. 158, p. 107408, 2021.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Tara Nawzad Ahmad Al Attar
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
