Yazar "Gharehchopogh, Farhad Soleimanian" seçeneğine göre listele

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    An improved African vultures optimization algorithm using different fitness functions for multi-level thresholding image segmentation
    (Springer, 2024) Gharehchopogh, Farhad Soleimanian; Ibrikci, Turgay
    Image segmentation is one of the most significant and required procedures in pre-processing and analyzing images. Metaheuristic optimization algorithms are used to solve a wide range of different problems because they can solve problems with different dimensions in an acceptable time and with quality results. It can show different functions in solving various problems. So, a metaheuristic algorithm should be adapted to solve the target problem with different mechanisms to find the best performance. In this paper, we have used the improved African Vultures Optimization Algorithm (AVOA) that uses the three binary thresholds (Kapur's entropy, Tsallis entropy, and Ostu's entropy) in multi-threshold image segmentation. The Quantum Rotation Gate (QRG) mechanism has increased population diversity in optimization stages, and optimal local trap escapes to improve AVOA performance. The Association Strategy (AS) mechanism is used to obtain and faster search for optimal solutions. These two mechanisms increase the diversity of production solutions in all optimization stages because the AVOA algorithm focuses on the exploration phase almost in the first half of the iterations. So, in this approach, it is possible to guarantee a wide variety of solutions and avoid falling into the local optimum trap. Standard criteria and datasets were used to evaluate the performance of the proposed algorithm and then compared with other optimization algorithms. Eight images with large dimensions have been used to evaluate the proposed algorithm so that the ability of the proposed algorithm and other compared algorithms can be accurately checked. A better solution to large-scale problems requires good performance of the algorithm in both the exploitation and exploration phases, and a balance must be created between these two phases. According to the experimental results from the proposed algorithm, it is determined that it has a good and significant performance.
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    Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications
    (Springer, 2023) Gharehchopogh, Farhad Soleimanian; Ucan, Alaettin; Ibrikci, Turgay; Arasteh, Bahman; Isik, Gultekin
    Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.