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Yayın Saliency detection based on hybrid artificial bee colony and firefly optimization(Springer Science and Business Media Deutschland GmbH, 2022-11) Yelmenoğlu, Elif Deniz; Çelebi, Numan; Taşçı, TuğrulSaliency detection is one of the challenging problems still tackled by image processing and computer vision research communities. Although not very numerous, recent studies reveal that optimization-based methods provide relatively accurate and fast solutions for such problems. This paper presents a novel unsupervised hybrid optimization method that aims to propose reasonable solution to saliency detection problem by combining the familiar artificial bee colony and firefly algorithms. The proposed method, HABCFA, is based on creating hybrid-personality individuals behaving like both bees and fireflies. A superpixel-based method is used to obtain better background intensity values in the saliency detection process, providing a better precision in extracting the salient regions. HABCFA algorithm is capable of achieving an optimum saliency map without requiring any extra mask or training step. HABCFA has produced superior performance against its basis algorithms, artificial bee colony, and firefly on four known benchmark problems regarding convergence rate and iteration count. On the other hand, the experimental results on four commonly used datasets, including MSRA-1000, ECSSD, ICOSEG, and DUTOMRON, demonstrate that HABCFA is adequately robust and effective in terms of accuracy, precision, and speed in comparison with the eleven state-of-the-art methods.Yayın 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, ŞirinMetaheuristic 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.
