Yazar "Karakoyun, Murat" seçeneğine göre listele

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    An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets
    (Ramazan Yaman, 2021) Mat, Ayse Nagehan; Inan, Onur; Karakoyun, Murat
    Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets taken from the UCI Machine Learning Repository database. The clustering performance of the WOA-LF was compared with the performance of k-means, k-medoids, fuzzy c-means and the original WOA clustering algorithms. Application results showed that WOA-LF has more successful clustering performance in general and can be used as an alternative algorithm in clustering problems.
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    A binary tree seed algorithm with selection-based local search mechanism for huge-sized optimization problems
    (Elsevier, 2022) Karakoyun, Murat; Ozkis, Ahmet
    Tree seed algorithm (TSA) is a recently proposed metaheuristic algorithm for solving continuous optimization problems. In order to use TSA in binary optimization problems, the SimLogicTSA method was developed by adding logic gates and Jaccard's similarity measure to this algorithm by Cinar and Kiran. Although SimLogicTSA is generally successful in small, medium, and large size problems, it has not been successful in the huge-sized problems by stucking into local minima. To overcome this problem, a new local search mechanism called enhanced local search module (ELSM) is proposed and the SimLogicTSA-ELSM algorithm is suggested by implementing the ELSM mechanism to the original SimLogicTSA algorithm. The proposed ELSM mechanism consists of a swap operator and logic-based gates. To analyze the contribution of the ELSM mechanism to the algorithm, firstly, the original SimLogicTSA and SimLogicTSA-ELSM algorithms were compared on the Cap and M* problem sets. The obtained results showed that the proposed algorithm produced more successful results than the original SimLogicTSA. Then, the proposed SimLogicTSA-ELSM is compared with many state-of -art algorithms in the literature by using different performance metrics on Cap and M* problem sets. The results show that SimLogicTSA-ELSM outperforms the compared algorithms in nearly all cases. Especially, the performance of the SimLogicTSA-ELSM stands out in huge-sized problems. (C) 2022 Elsevier B.V. All rights reserved.
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    Biyomedikal veri kümeleri ile makine öğrenmesi sınıflandırma algoritmalarının istatistiksel olarak karşılaştırılması
    (2014) Karakoyun, Murat; Hacibeyoğlu, Mehmet
    Günümüzde bilişim teknolojileri hemen hemen her alanda kullanılmaktadır. En çok kullanılan alanlardan bir tanesi de sağlık sektörüdür. Dijital hastane sistemlerinin kullanılmasıyla birlikte hasta verileri artık bilgisayar ortamında saklanmakta ve böylelikle biyomedikal veri kümeleri oluşmaktadır. Boyut olarak çok büyük olan bu veri kümelerinin bir insan tarafından analiz edilmesi ve yorumlanması çok zordur. Bunun için bilgisayar mühendisliği çalışma alanlarından biri olan makine öğrenmesi algoritmaları kullanılır. Bu çalışmada 6 tane makine öğrenmesi algoritmalarının başarımları 9 farklı biyomedikal veri kümesi üzerinde test edilmiştir ve elde edilen sonuçlar istatistiksel olarak karşılaştırılmıştır. Deneysel ve istatistiksel sonuçlar birlikte incelediğinde küçük ve orta büyüklükteki biyomedikal veri kümeleri için Yapay Sinir Ağları algoritması sınıflandırma başarımı açısından ve K- en Yakın Komşu algoritması ise çalışma zamanı açısından daha başarılı olmuştur. Bu çalışmanın bir bölümü ASYU 2014/İzmir sempozyumunda bildiri olarak sunulmuştur.
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    D-MOSG: Discrete multi-objective shuffled gray wolf optimizer for multi-level image thresholding
    (Elsevier - Division Reed Elsevier India Pvt Ltd, 2021) Karakoyun, Murat; Gulcu, Saban; Kodaz, Halife
    Segmentation is an important step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is a very popular approach. To apply the thresholding approach, many methods such as Otsu, Kapur, Renyi etc. have been proposed in order to produce the thresholds that will segment the image optimally. These suggested methods usually have their own characteristics and are successful for particular images. It can be thought that better results may be obtained by using objective functions with different characteristics together. In this study, the thresholding which is originally applied as a single-objective problem has been considered as a multi-objective problem by using the Otsu and Kapur methods. Therefore, the discrete multi-objective shuffled gray wolf optimizer (D-MOSG) algorithm has been proposed for multi-level thresholding segmentation. Experiments have clearly shown that the D-MOSG algorithm has achieved superior results than the compared algorithms. (C) 2021 Karabuk University. Publishing services by Elsevier B.V.
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    An improved butterfly optimization algorithm for training the feed-forward artificial neural networks
    (Springer, 2023) Irmak, Busra; Karakoyun, Murat; Gulcu, Saban
    Artificial neural network (ANN) which is an information processing technique developed by modeling the nervous system of the human brain is one of the most powerful learning methods today. One of the factors that make ANN successful is its training algorithm. In this paper, an improved butterfly optimization algorithm (IBOA) based on the butterfly optimization algorithm was proposed for training the feed-forward artificial neural networks. The IBOA algorithm has the chaotic property which helps optimization algorithms to explore the search space more dynamically and globally. In the experiments, ten chaotic maps were used. The success of the IBOA algorithm was tested on 13 benchmark functions which are well known to those working on global optimization and are frequently used for testing and analysis of optimization algorithms. The Tent-mapped IBOA algorithm outperformed the other algorithms in most of the benchmark functions. Moreover, the success of the IBOA-MLP algorithm also has been tested on five classification datasets (xor, balloon, iris, breast cancer, and heart) and the IBOA-MLP algorithm was compared with four algorithms in the literature. According to the statistical performance metrics (sensitivity, specificity, precision, F1-score, and Friedman test), the IBOA-MLP outperformed the other algorithms and proved to be successful in training the feed-forward artificial neural networks.
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    A new algorithm based on gray wolf optimizer and shuffled frog leaping algorithm to solve the multi-objective optimization problems
    (Elsevier, 2020) Karakoyun, Murat; Ozkis, Ahmet; Kodaz, Halife
    Multi-objective optimization is many important since most of the real world problems are in multiobjective category. Looking at the literature, the algorithms proposed for the solution of multi-objective problems have increased in recent years, but there is no a convenient approach for all kind of problems. Therefore, researchers aim to contribute to the literature by offering new approaches. In this study, an algorithm based on gray wolf optimizer (GWO) with memeplex structure of the shuffled frog leaping algorithm (SFLA), which is named as multi-objective shuffled GWO (MOSG), is proposed to solve the multi-objective optimization problems. Additionally, some modifications are applied on the proposed algorithm to improve the performance from different angles. The performance of the proposed algorithm is compared with the performance of six multi-objective algorithms on a benchmark set consist of 36 problems. The experimental results are presented with four different comparison metrics and statistical tests. According to the results, it can easily be said that the proposed algorithm is generally successful to solve the multi-objective problems and has better or competitive results. (C) 2020 Elsevier B.V. All rights reserved.