Tuned African Vultures Optimization Algorithm for Optimal Design of Skeletal Structures Employing Multi-Stage Parameter Adjustment

African Vultures Optimization Algorithm (AVOA) is a nature-inspired optimization method whose inspiration corresponds to the behaviors of African vultures. This algorithm consists of some parameters that switch different phases, significantly influencing the performance. In this paper, the tuned version of this algorithm, termed TAVOA, with the use of Multi-Stage Parameter Adjustment (MSPA) is implemented for the size optimization of skeletal structures. The MSPA integrates Metaheuristics, Extreme Latin Hypercube Sampling (XLHS), and Machine Learning for metaheuristic parameter adjustment leading to a better performance in the context of structural optimization. AVOA and its adjusted version are tested against the sizing optimization of two space trusses and two frame structures, and the results are compared to those of some state-of-the-art algorithms and their improved or enhanced versions. The findings underscore the effectiveness of MSPA in enhancing the performance of AVOA when confronted with diverse optimization challenges, with a particular emphasis on its applicability for performing optimal designs of skeletal structures for both continuous and discrete optimization problems.