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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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    The estimation of the electricity energy demand using particle swarm optimization algorithm: A case study of Turkey
    (Elsevier Science Bv, 2017) Gulcu, Saban; Kodaz, Halife
    Energy is the most important factor in improving the quality of life and advancing the economic and social progress. Demographic changes directly affect the energy demand. At present the worlds population is growing quickly. As of 2015, it was estimated at 7.3 billion. The population and the export of Turkey have been increasing for two decades. Consequently, electricity energy demand of Turkey has been increasing rapidly. This study aims to predict the future electricity energy demand of Turkey. In this paper, the prediction of the electricity demand of Turkey is modeled by using particle swarm optimization algorithm. The data of the gross domestic product, population, import and export are used as input data of the proposed model in the experiments. The GDP, import and export data are taken from the annual reports of the Turkish Ministry of Finance. The population data are taken from the Turkish Statistical Institute. The electricity demand data are taken from the Turkish Electricity Transmission Company. The statistical method R-2 and adjusted-R-2 are used as the performance criteria. The experimental results show that the generated model is very efficient. (c) 2017 The Authors. Published by Elsevier B.V.
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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.
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    A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization
    (Pergamon-Elsevier Science Ltd, 2015) Gulcu, Saban; Kodaz, Halife
    This article presented a parallel metaheuristic algorithm based on the Particle Swarm Optimization (PSO) to solve global optimization problems. In recent years, many metaheuristic algorithms have been developed. The PSO is one of them is very effective to solve these problems. But PSO has some shortcomings such as premature convergence and getting stuck in local minima. To overcome these shortcomings, many variants of PSO have been proposed. The comprehensive learning particle swarm optimizer (CLPSO) is one of them. We proposed a better variation of CLPSO, called the parallel comprehensive learning particle swarm optimizer (PCLPSO) which has multiple swarms based on the master-slave paradigm and works cooperatively and concurrently. The PCLPSO algorithm was compared with nine PSO variants in the experiments. It showed a great performance over the other PSO variants in solving benchmark functions including their large scale versions. Besides, it solved extremely fast the large scale problems. (C) 2015 Elsevier Ltd. All rights reserved.
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    A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem
    (Springer, 2018) Gulcu, Saban; Mahi, Mostafa; Baykan, Omer Kaan; Kodaz, Halife
    This article presented a parallel cooperative hybrid algorithm for solving traveling salesman problem. Although heuristic approaches and hybrid methods obtain good results in solving the TSP, they cannot successfully avoid getting stuck to local optima. Furthermore, their processing duration unluckily takes a long time. To overcome these deficiencies, we propose the parallel cooperative hybrid algorithm (PACO-3Opt) based on ant colony optimization. This method uses the 3-Opt algorithm to avoid local minima. PACO-3Opt has multiple colonies and a master-slave paradigm. Each colony runs ACO to generate the solutions. After a predefined number of iterations, each colony primarily runs 3-Opt to improve the solutions and then shares the best tour with other colonies. This process continues until the termination criterion meets. Thus, it can reach the global optimum. PACO-3Opt was compared with previous algorithms in the literature. The experimental results show that PACO-3Opt is more efficient and reliable than the other algorithms.
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    Prediction of Protein-Protein Interactions Using An Effective Sequence Based Combined Method
    (Elsevier, 2018) Goktepe, Yunus Emre; Kodaz, Halife
    Proteins and their interactions play a key role in the realization of all cellular biological activities of organisms. Therefore, prediction of protein-protein interactions is crucial for elucidating biological processes. Experimental studies are inadequate for some reasons such as the time required to reveal interactions, the fact that it is an expensive way and the number of yet unknown interactions is too great. Thus, a number of computational methods have been developed to predict protein-protein interactions. Generally, many of these methods that produce good results cannot be used without additional biological information such as protein domains, protein structural information, gene neighborhoods, gene expressions, and phylogenetic profiles. Therefore, there is a need for computational methods that can successfully predict interactions using only protein sequences. In this study, we present a novel sequence-based computational model. We applied a new technique called weighted skip-sequential conjoint triads in the proposed method. The results of this research were evaluated on generally used databases and demonstrated its success in this field. (C) 2018 Elsevier B.V. All rights reserved.