Browsing by Author "Altay O."
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Item Chaotic slime mould optimization algorithm for global optimization(Springer Science and Business Media B.V., 2022) Altay O.Metaheuristic optimization methods; It is a well-known global optimization approach for large-scale search and optimization problems, commonly used to find the solution many different optimization problems. Slime mould optimization algorithm (SMA) is a recently presented metaheuristic technique that is inspired by the behavior of slime mould. Slow convergence speed is a fundamental problem in SMA as in other metaheuristic optimization methods. In order to improve the SMA method, 10 different chaotic maps have been applied for the first time in this article to generate chaotic values instead of random values in SMA. Using chaotic maps, it is aimed to increase the speed of SMA’s global convergence and prevent it from getting stuck in its local solutions. The Chaotic SMA (CSMA) proposed for the first time in this study was applied to 62 different benchmark functions. These are unimodal, multimodal, fixed dimension, CEC2019, and CEC2017 test suite. The results of the application have been comparatively analyzed and statistical analysis performed with the well-known metaheuristic optimization methods, particle swarm optimization and differential evolution algorithm, and recently proposed grey wolf optimization (GWO) and whale optimization algorithm (WOA). In addition, in the CEC2017 test suite, the CSMA method has been compared with the SMA, WOA, GWO, harris hawk optimization, archimedes optimization algorithm and COOT algorithms that have been proposed in recent years, and statistical analyzes have been made. In addition, CSMA has been tested in 3 different real-world engineering design problems. According to the experimental results, it was observed that CSMA achieved relatively more successful results in 62 different benchmark functions and real-world engineering design problems compared to other compared methods and standard SMA. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.Item Surface roughness prediction of wire electric discharge machining (WEDM)-machined AZ91D magnesium alloy using multilayer perceptron, ensemble neural network, and evolving product-unit neural network(Walter de Gruyter GmbH, 2022) Gurgenc T.; Altay O.Magnesium (Mg) alloy parts have become very interesting in industries due to their lightness and high specific strengths. The production of Mg alloys by conventional manufacturing methods is difficult due to their high affinity for oxygen, low melting points, and flammable properties. These problems can be solved using nontraditional methods such as wire electric discharge machining (WEDM). The parts with a quality surface have better properties such as fatigue, wear, and corrosion resistance. Determining the surface roughness (SR) by analytical and experimental methods is very difficult, time-consuming, and costly. These disadvantages can be eliminated by predicting the SR with artificial intelligence methods. In this study, AZ91D was cut with WEDM in different voltage (V), pulse-on-time (μs), pulse-off-time (μs), and wire speed (mm s-1) parameters. The SR was measured using a profilometer, and a total of 81 data were obtained. Multilayer perceptron, ensemble neural network and optimization-based evolving product-unit neural network (EPUNN) were used to predict the SR. It was observed that the EPUNN method performed better than the other two methods. The use of this model in industries producing Mg alloys with WEDM expected to provide advantages such as time, material, and cost. © 2022 Walter de Gruyter GmbH, Berlin/Boston.Item Hybrid artificial neural network based on a metaheuristic optimization algorithm for the prediction of reservoir temperature using hydrogeochemical data of different geothermal areas in Anatolia (Turkey)(Elsevier Ltd, 2022) Varol Altay E.; Gurgenc E.; Altay O.; Dikici A.Due to the increase in the changes in global climate in recent years and the depletion of fossil fuels, the interest in renewable energy sources in many developed countries is increasing day by day. Among the renewable energy sources, geothermal energy has an important place because it can be used both in electricity production and directly as heat energy. Before using geothermal fluids, it is necessary to determine their properties by making detailed geological studies and thus to determine the most suitable drilling location. These processes are very costly, time-consuming, and require special equipment. Such disadvantages can be eliminated by using machine learning methods. In this study, the machine learning methods developed for the classification approach were used to predict the purpose of the geothermal waters with the help of the geothermal data set obtained from different regions. In this study, naïve Bayes classifier, K-nearest neighbor, linear discrimination analysis, binary decision tree, support vector machine, and artificial neural network, which are widely used in the literature, were used. In addition, promising results were obtained by designing a hybrid metaheuristic artificial neural network model. While an accuracy in traditional machine learning methods between 71% and 82% was obtained, a 91.84% accuracy was obtained in the model proposed. © 2022 Elsevier LtdItem Performance of different KNN models in prediction english language readability(Institute of Electrical and Electronics Engineers Inc., 2022) Altay O.Assessing the readability of English, a universal language, is important in terms of meeting readers at different reading levels with texts at their own level. Presenting texts to readers at their own level will help them develop their learning, comprehension and reading capacities. In this study, a data set collected from BBC news was used to predict the readability of the English language. The data set consists of 17724 different sentences. Different k-nearest neighbor (KNN) models were used to predict the readability of English sentences. These models are basic KNN, two different weighted KNN and KNN base random subspace ensembles. KNN base random subspace ensemble has obtained superior results compared to other KNN models. KNN base random subspace ensemble accuracy was 0.9749 and f1-score 0.9692. © 2022 IEEE.Item MetricHunter: A software metric dataset generator utilizing SourceMonitor upon public GitHub repositories(Elsevier B.V., 2023) Özçevik Y.; Altay O.Version control systems are pervasively consulted nowadays to obtain software metric datasets. Accordingly, machine learning is applied to predict different aspects of a software including quality monitoring, influence analysis, etc. However, construction of a metric dataset is challenging and the dataset content may affect the success of the learning-based models. In this study, we propose a dataset construction tool, MetricHunter, which is able to produce platform/language specific datasets that can be used for predicting the features of newly created software. The proposed tool is developed by C# programming language utilizing a known metric gathering tool, i.e. SourceMonitor, and the GitHub REST API for public repositories. Thus, one can construct a proper dataset from a graphical user interface by simply specifying the programming language or target platform. The outputs of the tool on a set of repositories are validated by investigating automatically generated attribute values and comparing them with the measurements of metric gathering tools as well as the GitHub metric values. © 2023 The Author(s)Item Assessment of Grey Wolf Optimizer and Its Variants on Benchmark Functions(Springer Science and Business Media Deutschland GmbH, 2023) Varol Altay E.; Altay O.One of the most current metaheuristic swarm intelligence algorithms is the Grey Wolf Optimizer (GWO). Since the number of parameters is small and there is no need for information during the first search, GWO has been adapted to different optimization problems, giving it superiority over other metaheuristic methods. At the same time, it is easy to use, simple, scalable, and adaptable, with its unique ability to provide the ideal balance throughout the search, leading to positive convergence. As a result, the GWO has lately attracted a large research audience from a variety of areas in a very short amount of time. However, it has some disadvantages, such as a slow convergence rate, low sensitivity, and so on, which are seen in the vast majority of metaheuristic methods. Various versions of the current GWO have been proposed to eliminate them. In this article, GWO, improved GWO (IGWO), and augmented GWO (AGWO) methods are examined, and the performances of these methods are discussed in CEC’20 functions and analyzed statistically. The results of the studies demonstrated that IGWO outperformed standard GWO and AGWO. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item A novel chaotic transient search optimization algorithm for global optimization, real-world engineering problems and feature selection(PeerJ Inc., 2023) Altay O.; Altay E.V.Metaheuristic optimization algorithms manage the search process to explore search domains efficiently and are used efficiently in large-scale, complex problems. Transient Search Algorithm (TSO) is a recently proposed physics-based metaheuristic method inspired by the transient behavior of switched electrical circuits containing storage elements such as inductance and capacitance. TSO is still a new metaheuristic method; it tends to get stuck with local optimal solutions and offers solutions with low precision and a sluggish convergence rate. In order to improve the performance of metaheuristic methods, different approaches can be integrated and methods can be hybridized to achieve faster convergence with high accuracy by balancing the exploitation and exploration stages. Chaotic maps are effectively used to improve the performance of metaheuristic methods by escaping the local optimum and increasing the convergence rate. In this study, chaotic maps are included in the TSO search process to improve performance and accelerate global convergence. In order to prevent the slow convergence rate and the classical TSO algorithm from getting stuck in local solutions, 10 different chaotic maps that generate chaotic values instead of random values in TSO processes are proposed for the first time. Thus, ergodicity and non-repeatability are improved, and convergence speed and accuracy are increased. The performance of Chaotic Transient Search Algorithm (CTSO) in global optimization was investigated using the IEEE Congress on Evolutionary Computation (CEC)’17 benchmarking functions. Its performance in real-world engineering problems was investigated for speed reducer, tension compression spring, welded beam design, pressure vessel, and three-bar truss design problems. In addition, the performance of CTSO as a feature selection method was evaluated on 10 different University of California, Irvine (UCI) standard datasets. The results of the simulation showed that Gaussian and Sinusoidal maps in most of the comparison functions, Sinusoidal map in most of the real-world engineering problems, and finally the generally proposed CTSOs in feature selection outperform standard TSO and other competitive metaheuristic methods. Real application results demonstrate that the suggested approach is more effective than standard TSO. © 2023 Altay and Varol AltayItem A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems(Tech Science Press, 2023) Altay E.V.; Altay O.; Özçevik Y.Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve. Such design problems are widely experienced in many engineering fields, such as industry, automotive, construction, machinery, and interdisciplinary research. However, there are established optimization techniques that have shown effectiveness in addressing these types of issues. This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues. The algorithms used in the study are listed as: transient search optimization (TSO), equilibrium optimizer (EO), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale optimization algorithm (WOA), slime mould algorithm (SMA), harris hawks optimization (HHO), chimp optimization algorithm (COA), coot optimization algorithm (COOT), multi-verse optimization (MVO), arithmetic optimization algorithm (AOA), aquila optimizer (AO), sine cosine algorithm (SCA), smell agent optimization (SAO), and seagull optimization algorithm (SOA), pelican optimization algorithm (POA), and coati optimization algorithm (CA). As far as we know, there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems. Hence, a remarkable research guideline is presented in the study for researchers working in the fields of engineering and artificial intelligence, especially when applying the optimization methods that have emerged recently. Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions. © 2023 Tech Science Press. All rights reserved.Item A novel hybrid multilayer perceptron neural network with improved grey wolf optimizer(Springer Science and Business Media Deutschland GmbH, 2023) Altay O.; Varol Altay E.The multilayer perceptron (MLP), a type of feed-forward neural network, is widely used in various artificial intelligence problems in the literature. Backpropagation is the most common learning method used in MLPs. The gradient-based backpropagation method, which is one of the classical methods, has some disadvantages such as entrapment in local minima, convergence speed, and initialization sensitivity. To eliminate or minimize these disadvantages, there are many studies in the literature that use metaheuristic optimization methods instead of classical methods. These methods are constantly being developed. One of these is an improved grey wolf optimizer (IMP-GWO) proposed to eliminate the disadvantages of the grey wolf optimizer (GWO), which suffers from a lack of search agent diversity, premature convergence, and imbalance between exploitation and exploration. In this study, a new hybrid method, IMP-GWO-MLP, machine learning method was designed for the first time by combining IMP-GWO and MLP. IMP-GWO was used to determine the weight and bias values, which are the most challenging parts of the MLP training phase. The proposed IMP-GWO-MLP was applied to 20 datasets consisting of three different approximations, eight regression problems, and nine classification problems. The results obtained have been suggested in the literature and compared with the gradient descent-based MLP, commonly used GWO, particle swarm optimization, whale optimization algorithm, ant lion algorithm, and genetic algorithm-based MLP methods. The experimental results show that the proposed method is superior to other state-of-the-art methods in the literature. In addition, it is thought that the proposed method can be modeled with high success in real-world problems. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Item AOSMA-MLP: A Novel Method for Hybrid Metaheuristics Artificial Neural Networks and a New Approach for Prediction of Geothermal Reservoir Temperature(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Gurgenc E.; Altay O.; Altay E.V.Featured Application: The proposed models can help uncover the usage areas of geothermal waters by determining the reservoir temperatures in advance. Thus, they can be used as a decision support system to make the most appropriate selection. To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R2 value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems. © 2024 by the authors.Item Average Localization Error Prediction for 5G Networks: An Investigation of Different Machine Learning Algorithms(Springer, 2024) Altay O.; Erel-Özçevik M.; Varol Altay E.; Özçevik Y.In the realm of today’s networking technologies, user localization has been a formidable challenge for recent applications. There are different approaches in pursuit of heightened position detection of an end-user with the help of GPS, Wi-Fi fingerprint and 5G equipment. However, these approaches require both deployment and maintenance costs because of equipment establishment for position tracking. Moreover, they are not capable of minimizing the localization error, especially for indoor scenarios to track the indoor position of an end-user. Hence, there is an urgent need to delve deeper into innovative approaches to drive further advancements in user localization. In response, Machine Learning (ML) approaches have recently been widely adapted to predict the localization of end-users with minimum error. More specifically, average localization error (ALE) of an end-user can be predicted in a cost-effective way by using proper data and ML methods. For this purpose, we have investigated different ML approaches to get an accurate ALE prediction scheme for 5G networks with mobile end-users. Accordingly, an existing dataset is utilized to generate localization data of end-users in which the ALE is directly calculated by Received Signal Strength Indicator. Moreover, three different normalization approaches are applied for the overarching goal of increased data quality. Consequently, six different ML algorithms, including Linear regression, support vector machine with three different kernels, Gaussian process, and ensemble least-squares boosting (LSBoost) are evaluated with respect to a set of evaluation criteria including R, R2, RMSE, and MAE. The evaluation outcomes emphasize that ensemble LSBoost method, in the context of localization prediction, outperforms the other approaches and is sufficient to yield a viable learning strategy for ALE prediction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.Item GJO-MLP: A NOVEL METHOD FOR HYBRID METAHEURISTICS MULTI-LAYER PERCEPTRON AND A NEW APPROACH FOR PREDICTION OF WEAR LOSS OF AZ91D MAGNESIUM ALLOY WORN AT DRY, OIL, AND h-BN NANOADDITIVE OIL(World Scientific, 2024) Altay O.; Gurgenc T.In this study, the AZ91D magnesium alloy was worn at different wear conditions (dry, oil, and h-BN nanoadditive oil), loads (10–60 N), sliding speeds (50–150 mm/s) and sliding distances (100–1000 m). Wear losses increased with the increase of applied load, sliding speed, and sliding distance. Wear losses were decreased in the h-BN nanoadditive oil conditions. For the first time, the wear losses were predicted using the hybrid golden jackal optimizer-multi-layer perceptron (GJO-MLP) method proposed in this study, using the experimentally obtained data. In addition, the performance of the proposed method was compared with the whale optimization-MLP (WOA-MLP), genetic algorithm-MLP (GA-MLP) and ant lion optimization-MLP (ALO-MLP) methods, which are widely used in the literature. The results showed that GJO-MLP outperformed other methods with a performance of 0.9784 in R2 value. © World Scientific Publishing Company.