A Hybrid Genetic Algorithm-Particle Swarm Optimization Approach for Enhanced Text Compression

Abstract

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.

Keywords

• text compression
• genetic algorithms
• particle swarm optimization
• hybrid algorithm
• data storage