Finding the best solutions is essential for obtaining the desired outcomes in today’s situation, where optimization problems arise in many different domains. Due to their effectiveness in quickly exploring solution areas and delivering close to optimal solutions, nature-inspired optimization algorithms have attracted a lot of interest. In this research paper, we present a comparative study of several popular nature-inspired optimization algorithms (Pati et al. in Neural Comput Appl 33:10,751–10,770, 2021). These algorithms are highly efficient in finding optimized solutions to multi-dimensional and multi-modal problems. The findings of this study can direct practitioners and researchers in choosing suitable test case generation algorithm and encourage further development in the software testing discipline. In the era of digital world, automation plays a vital role and “Test Automation Framework” (Padhy et al. in 2023 Springer nature Singapore international conference on ICT for sustainable development, 2021) is again very important part of doing automation.