Baş, Emine2024-09-182024-09-182023978-625-6404-53-3https://hdl.handle.net/20.500.13091/6239Metaheuristic algorithms are of great importance in solving optimization problems. In this study, the newly proposed African Vulture Optimization algorithm (AVO) has been examined. The AVO algorithm mimics the life-styles of African vultures and was created by imitating the foraging and wandering behavior of African vultures. Six kinds of fixed parameters (P1, P2, P3, L1, L2, w) are used in the algorithm. While the original paper examined the effect of these parameter values on AVO for only six types of values, nine types of effects were examined in this study (L1={0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1}, L2={0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}, P1={0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}, P2={0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}, P3={0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}, w= {1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5}). The best parameter values were selected for AVO by examining the results. These parameters balance AVO's local and global search capabilities. According to the results, while the values of L1, L2, and w parameters were similar to the values in the original paper (L1=0.6, L2=0.4, and w=2.5), different appropriate values were determined for P1, P2, and P3 values (P1=0.4, P2=0.9, and P3=0.6).eninfo:eu-repo/semantics/openAccessAfrican vulturesOptimizationAVOConference Object