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Öğe 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, HalifeSegmentation 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.Öğe The estimation of the electricity energy demand using particle swarm optimization algorithm: A case study of Turkey(Elsevier Science Bv, 2017) Gulcu, Saban; Kodaz, HalifeEnergy 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.Öğe An Improved Animal Migration Optimization Algorithm to Train the Feed-Forward Artificial Neural Networks(Springer Heidelberg, 2022) Gulcu, SabanThe most important and demanding part of the artificial neural network is the training process which involves finding the most suitable values for the weights in the network architecture, a challenging optimization problem. Gradient approaches and the meta-heuristic approaches are two methods extensively used to optimize the weights in the network. Gradient approaches have serious disadvantages including getting stuck in local optima, inadequate exploration, etc. To overcome these disadvantages, meta-heuristic approaches are preferred in training the artificial neural network instead of gradient methods. Therefore, in this study, an improved animal migration optimization algorithm with the Levy flight feature was proposed to train the multilayer perceptron. The proposed hybrid algorithm is named IAMO-MLP. The main contributions of this article are that the IAMO algorithm was developed, the IAMO-MLP algorithm can successfully escape from local optima, and the initial positions did not affect the performance of the IAMO-MLP algorithm. The enhanced algorithm was tested and validated against a wider set of benchmark functions and indicated that it substantially outperformed the original implementation. Afterward, the IAMO-MLP was compared with ten algorithms on five classification problems (xor, balloon, iris, breast cancer, and heart) and one real-world problem in terms of mean squared error, classification accuracy, and nonparametric statistical Friedman test. According to the results, the IAMO was successful in training the multilayer perceptron.Öğe An improved butterfly optimization algorithm for training the feed-forward artificial neural networks(Springer, 2023) Irmak, Busra; Karakoyun, Murat; Gulcu, SabanArtificial 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.Öğe Modeling of Removal of Chromium (VI) from Aqueous Solutions Using Artificial Neural Network(Jihad Daneshgahi, 2020) Tumer, Erdal Abdullah; Edebali, Serpil; Gulcu, SabanThere is a need for knowledge, experience, laboratory, materials, and time to conduct chemical experiments. The results depend on the process and are also quite costly. For economic and rapid results, chemical processes can be modeled by utilizing data obtained in the past. In this paper, an artificial neural network model is proposed for predicting the removal efficiency of Cr (VI) from aqueous solutions with Amberlite IRA-96 resin, as being independent of chemical processes. Multiple linear regression, linear and quadratic particle swarm optimization are also used to compare prediction success. A total of 34 experimental data were used for training and validation of the model. pH, amount of resin, contact time, and concentration were used as input data. The removal efficiency is considered as output data for each model. The statistical methods of root-mean-square error, mean absolute percentage error, variance absolute relative error, and the coefficient of determination were used to evaluate the performance of the developed models. The system has been analyzed using a feature selection method to assess the influence of input parameters on the sorption efficiency. The most significant factor was found in pH. The obtained results show that the proposed ANN model is more reliable than the other models for estimating removal efficiency.Öğe A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization(Pergamon-Elsevier Science Ltd, 2015) Gulcu, Saban; Kodaz, HalifeThis 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.Öğe Optimization of flexure stiffness of FGM beams via artificial neural networks by mixed FEM(Techno-Press, 2020) Madenci, Emrah; Gulcu, SabanArtificial neural networks (ANNs) are known as intelligent methods for modeling the behavior of physical phenomena because of it is a soft computing technique and takes data samples rather than entire data sets to arrive at solutions, which saves both time and money. ANN is successfully used in the civil engineering applications which are suitable examining the complicated relations between variables. Functionally graded materials (FGMs) are advanced composites that successfully used in various engineering design. The FGMs are nonhomogeneous materials and made of two different type of materials. In the present study, the bending analysis of functionally graded material (FGM) beams presents on theoretical based on combination of mixed-finite element method, Gateaux differential and Timoshenko beam theory. The main idea in this study is to build a model using ANN with four parameters that are: Young's modulus ratio (E-t/E-b), a shear correction factor (k(s)), power-law exponent (n) and length to thickness ratio (L/h). The output data is the maximum displacement (w). In the experiments: 252 different data are used. The proposed ANN model is evaluated by the correlation of the coefficient (R), MAE and MSE statistical methods. The ANN model is very good and the maximum displacement can be predicted in ANN without attempting any experiments.Öğe Optimization of flexure stiffness of FGM beams via artificial neural networks by mixed FEM(Techno-Press, 2020) Madenci, Emrah; Gulcu, SabanArtificial neural networks (ANNs) are known as intelligent methods for modeling the behavior of physical phenomena because of it is a soft computing technique and takes data samples rather than entire data sets to arrive at solutions, which saves both time and money. ANN is successfully used in the civil engineering applications which are suitable examining the complicated relations between variables. Functionally graded materials (FGMs) are advanced composites that successfully used in various engineering design. The FGMs are nonhomogeneous materials and made of two different type of materials. In the present study, the bending analysis of functionally graded material (FGM) beams presents on theoretical based on combination of mixed-finite element method, Gateaux differential and Timoshenko beam theory. The main idea in this study is to build a model using ANN with four parameters that are: Young's modulus ratio (E-t/E-b), a shear correction factor (k(s)), power-law exponent (n) and length to thickness ratio (L/h). The output data is the maximum displacement (w). In the experiments: 252 different data are used. The proposed ANN model is evaluated by the correlation of the coefficient (R), MAE and MSE statistical methods. The ANN model is very good and the maximum displacement can be predicted in ANN without attempting any experiments.Öğe 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, HalifeThis 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.Öğe Training of the feed forward artificial neural networks using dragonfly algorithm(Elsevier, 2022) Gulcu, SabanOne of the most important parts of an artificial neural network (ANN) which affects performance is training algorithms. Training algorithms optimize the weights and biases of the ANN according to the inputs-outputs pattern. Two types of training algorithms are widely used: Gradient methods and meta-heuristic methods. Gradient methods are effective in training the ANN. But they have some disadvantages. The main disadvantage of gradient methods is premature convergence. Secondly, the performance of gradient methods highly depends on the initial parameters and positions. Thirdly, they can easily get stuck in local optima. To overcome these disadvantages, this article presents a new hybrid algorithm (DA-MLP) to train the feed-forward multilayer neural networks (MLP) using the dragonfly algorithm. The dragonfly algorithm optimizes the weights and biases of the MLP. In the experiments, one real-world problem in the civil engineering area and eight classification datasets were used. To verify the success of the DA-MLP algorithm, the results of the DA-MLP algorithm were compared with the results of four algorithms (the BAT-MLP based on the bat optimization algorithm, the SMS-MLP based on the states of matter search optimization algorithm, the PSO-MLP based on the particle swarm optimization algorithm, and the backpropagation (BP) algorithm). The experimental study showed that the DA-MLP algorithm is more efficient than the other algorithms. (c) 2022 Elsevier B.V. All rights reserved.
