## 1 Introduction
[9] introduces a hybrid method that couples a classical GA with a probabilistic local search, effectively minimizing the weight of truss structures. The Firefly Algorithm was formalized by [PERSON] in 2010 [10], drawing inspiration from fireflies' bioluminescent signaling. In FA, each firefly symbolizes a candidate solution, with its \"brightness\" proportional to the objective function value. Fireflies of lower brightness are drawn toward those of higher brightness (often referred to as the \"queen\" firefly), traversing the search space according to attractive forces that diminish with distance. Key parameters govern light-attenuation rates, thereby regulating convergence speed and the balance between global exploration and local exploitation. Owing to its capacity to explore complex, multimodal landscapes, FA has demonstrated competitive performance on a variety of optimization challenges [11; 12; 13; 14]. The Group Search Optimizer, introduced by [PERSON] et al. in 2006 [15], is inspired by the foraging strategies observed in animal groups. In structural engineering, [PERSON] and [PERSON] [16] extended GSO to multi-objective structural design, balancing criteria such as weight, stiffness, and natural frequency. GSO categorizes individuals into three roles (producers, scroungers, and rangers) to manage exploration and exploitation. Producersexecute directed searches based on angular and distance criteria to locate high-quality solutions. Scroupgers follow successful producers, exploiting discovered optima, while rangers perform random walks that promote diversity. This social-dynamic framework ensures a robust trade-off between intensification (exploitation) and diversification, facilitating effective global exploration. Accordingly, GSO has exhibited strong performance in civil and mechanical engineering structural optimization problems [17; 18; 19]. Although GA, FA, and GSO differ in their biological inspirations and algorithmic details, their success hinges upon careful parameter calibration and a dynamic balance between the exploration and exploitation phases. In recent years, numerical modeling has become a key tool for the analysis and optimization of complex systems. In particular, ANSYS [20] has established itself as a versatile and robust simulation environment, capable of tackling structural mechanics, fluid dynamics, or heat transfer problems across a wide range of application domains. The present work examines the integration of these three nature-inspired metaheuristics within ANSYS 2020 R2 APDL for structural optimization. Embedding the algorithms directly in APDL routines obviates the need for external coupling through Python (3.13.0)-based interfaces (e.g., PyANSYS (release 0.69) [21]), ensuring seamless compatibility with ANSYS solvers and reducing communication overhead. This native implementation enhances computational efficiency, particularly in high-fidelity simulations with large finite element models, where the communication and the data transfer can substantially affect overall performance. Furthermore, APDL-based integration affords tighter control over the simulation workflow and enables resource-efficient automation. The primary objective of this work is to provide a comparative analysis of GA, FA, and GSO when applied to structural optimization tasks while assessing the feasibility and benefits of a fully APDL-embedded solution. In addition, novel \"ranger-movement\" strategies are introduced for both FA and GSO. Instead of fixed, homogeneous displacement rules, each firefly or ranger may select among four distinct motion modes, thereby dynamically adjusting the balance between global exploration and local exploitation. This adaptive mechanism enables the algorithms to escape local minima and concentrate search effort in promising regions, yielding more robust and efficient convergence behaviors across multiple case studies. To validate the proposed framework, the algorithms are applied to benchmark spatial truss structures under prescribed loads.