In the field of structural engineering, the optimization process is crucial for ensuring both the efficiency and safety of designed structures. The persistent search for solutions that maximize structural performance while minimizing costs presents a fascinating and complex challenge. In this context, this paper aims to analyze and compare three different Nature-inspired optimization algorithms that are derived from theory of biological evolution, swarm intelligence, and chemical and physical processes. Specifically, the study aims to provide a comprehensive overview of the performance and characteristics of Genetic Algorithms, the Firefly Algorithm, and the Group Search Optimizer Algorithm in the context of structural optimization within the ANSYS APDL environment. The results obtained from each meta-heuristic algorithm will be discussed and compared in terms of their convergence, efficiency, and capability to find optimal and/or approximate solutions.