Bilgisayar ve Bilişim Fakültesi Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/10834

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Browsing Bilgisayar ve Bilişim Fakültesi Koleksiyonu by Subject "African vultures"

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    Conference Object
    Binary African Vultures Optimization Algorithm for Z-Shaped Transfer Functions
    (2023) Baş, Emine
    Metaheuristic algorithms are of great importance in solving binary optimization problems. African Vulture Optimization algorithm (AVO) is a swarm intelligence-based heuristic algorithm created by imitating the life forms of African vultures. In this study, the AVO, which has been proposed in recent years, is restructured to solve binary optimization problems. Thus, Binary AVO (BAVO) has been proposed. Four different z-shaped transfer functions are chosen to convert the continuous search space to binary search space. Variations for BAVO are defined according to the transfer function used (BAVO1, BAVO2, BAVO3, and BAVO4). The success of these variations was tested in thirteen classic test functions containing unimodal and multimodal functions. Three different dimensions were determined in the study (5, 10, and 20). Each test function was run ten times independently and the average, standard deviation, best, and worst values were obtained. According to the results obtained, the most successful of these variations has been identified. According to the results, the BAVO4 variant at higher dimensions achieved better results. The success of BAVO with z-shaped transfer functions was demonstrated for the first time in this study.
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    Conference Object
    Detailed Parameter Analysis for African Vultures Optimization Algorithm
    (2023) Baş, Emine
    Metaheuristic 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).