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Yazar: Yelmenoğlu, Elif Deniz × search.filters.applied.f.publicationcategory: Konferans Öğesi - Ulusal - Kurum Öğretim Elemanı ×

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    Hybridization strategies in swarm intelligence: the case of ABC–FA and ABC–RUN algorithms
    (BZT Turan Publishing House, 2025-10-08) Yelmenoğlu, Elif Deniz; Pajenado, Rex S.; Dilli, Şirin
    Metaheuristic optimization algorithms have become a very popular field of study in recent years due to their ability to effectively solve complex, multidimensional problems. In this study, the Artificial Bee Colony (ABC), Firefly Algorithm (FA), and Runge–Kutta (RUN) optimization algorithms, known for their good performance among metaheuristic methods, are compared with their hybrid variants ABC_RUN and ABC_FA. Five widely used benchmark functions were selected for performance evaluation, and the performance results of the algorithms were statistically evaluated using the Wilcoxon signed-rank test. Furthermore, convergence curves were generated to show the average performance of the algorithms, and average running times were calculated to examine the balance between accuracy and computational cost. The findings show that hybrid methods provide higher accuracy compared to classical methods, while the RUN algorithm has an advantage in terms of running time. This comparative analysis demonstrates that hybrid approaches can more effectively balance exploration and exploitation, increase global optimization performance, and are applicable to real-world problems.
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    A novel hybrid RUN-ABC optimization algorithm
    (BZT Turan Publishing House, 2025-10-08) Yelmenoğlu, Elif Deniz; Pajenado, Rex S.; Dilli, Şirin
    In recent years, with the development of technology, complex and high-dimensional problems have increased. The use of metaheuristic optimization algorithms in solving these complex problems has become an important research area. In this study, a new hybrid RUN-ABC optimization algorithm was developed by combining the RUN (Runge Kutta Optimization) algorithm and the ABC (Artificial Bee Colony) algorithm. By taking into account the powerful exploration capabilities of the ABC algorithm and the efficient exploitation capabilities of the RUN algorithm, the aim was to search for the best solution in a more balanced manner in the search space. Experiments were conducted on five different benchmark functions to evaluate the performance of the hybrid RUN-ABC method. In these experiments, the developed hybrid method ABC and RUN algorithms were compared based on the average best value, standard deviation, and convergence rate. Furthermore, the Wilcoxon signed-rank test (signrank) was applied to measure the performance between the algorithms. The results showed that the developed hybrid RUN-ABC algorithm outperformed both the RUN and ABC algorithms in most cases. The developed method demonstrated impressive performance in terms of achieving a global minimum and the stability of its results. This study demonstrates that the developed hybrid RUN-ABC method can be a powerful alternative and provides a basis for its future use in solving various complex problems.