<p>Software testing is a critical phase in software development that ensures the identification and resolution of faults. Automating test data generation can significantly reduce costs and improve efficiency. While genetic algorithms and particle swarm optimization (PSO) are widely used in this domain, recent research has introduced alternative approaches with enhanced performance. This paper presents an improved version of the Improved Lyrebird Optimization Algorithm (ILOA) for automated test data generation, aiming to enhance its exploration and exploitation capabilities. A novel fitness function is introduced to optimize the search process and improve test case generation. The proposed method is evaluated on multiple benchmark programs, and the experimental results demonstrate that ILOA outperforms existing state-of-the-art algorithms in terms of the number of fitness evaluations, execution time, test generation efficiency, coverage rate, and success rate. These findings highlight the effectiveness of ILOA in improving test data generation for software testing.</p>