Bilgisayar ve Bilişim Fakültesi Koleksiyonu

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Citation - WoS: 29
Citation - Scopus: 54
Alexnet Architecture Variations With Transfer Learning for Classification of Wound Images
(Elsevier B.V., 2023) Eldem, H.; Ülker, E.; Işıklı, O.Y.
In medical world, wound care and follow-up is one of the issues that are gaining importance to work on day by day. Accurate and early recognition of wounds can reduce treatment costs. In the field of computer vision, deep learning architectures have received great attention recently. The achievements of existing pre-trained architectures for describing (classifying) data belonging to many image sets in the real world are primarily addressed. However, to increase the success of these architectures in a certain area, some improvements and enhancements can be made on the architecture. In this paper, the classification of pressure and diabetic wound images was performed with high accuracy. The six different new AlexNet architecture variations (3Conv_Softmax, 3Conv_SVM, 4Conv_Softmax, 4Conv_SVM, 6Conv_Softmax, 6Conv_SVM) were created with a different number of implementations of Convolution, Pooling, and Rectified Linear Activation (ReLU) layers. Classification performances of the proposed models are investigated by using Softmax classifier and SVM classifier separately. A new original Wound Image Database are created for performance measures. According to the experimental results obtained for the Database, the model with 6 Convolution layers (6Conv_SVM) was the most successful method among the proposed methods with 98.85% accuracy, 98.86% sensitivity, and 99.42% specificity. The 6Conv_SVM model was also tested on diabetic and pressure wound images in the public medetec dataset, and 95.33% accuracy, 95.33% sensitivity, and 97.66% specificity values were obtained. The proposed method provides high performance compared to the pre-trained AlexNet architecture and other state-of-the-art models in the literature. The results showed that the proposed 6Conv_SVM architecture can be used by the relevant departments in the medical world with good performance in medical tasks such as examining and classifying wound images and following up the wound process. © 2023 Karabuk University
Citation - WoS: 30
Citation - Scopus: 36
Binary Aquila Optimizer for 0-1 Knapsack Problems
(Pergamon-Elsevier Science Ltd, 2023) Baş, Emine
The optimization process entails determining the best values for various system characteristics in order to finish the system design at the lowest possible cost. In general, real-world applications and issues in artificial intelligence and machine learning are discrete, unconstrained, or discrete. Optimization approaches have a high success rate in tackling such situations. As a result, several sophisticated heuristic algorithms based on swarm intelligence have been presented in recent years. Various academics in the literature have worked on such algorithms and have effectively addressed many difficulties. Aquila Optimizer (AO) is one such algorithm. Aquila Optimizer (AO) is a recently suggested heuristic algorithm. It is a novel population-based optimization strategy. It was made by mimicking the natural behavior of the Aquila. It was created by imitating the behavior of the Aquila in nature in the process of catching its prey. The AO algorithm is an algorithm developed to solve continuous optimization problems in their original form. In this study, the AO structure has been updated again to solve binary optimization problems. Problems encountered in the real world do not always have continuous values. It exists in problems with discrete values. Therefore, algorithms that solve continuous problems need to be restructured to solve discrete optimization problems as well. Binary optimization problems constitute a subgroup of discrete optimization problems. In this study, a new algorithm is proposed for binary optimization problems (BAO). The most successful BAO-T algorithm was created by testing the success of BAO in eight different transfer functions. Transfer functions play an active role in converting the continuous search space to the binary search space. BAO has also been developed by adding candidate solution step crossover and mutation methods (BAO-CM). The success of the proposed BAO-T and BAO-CM algorithms has been tested on the knapsack problem, which is widely selected in binary optimization problems in the literature. Knapsack problem examples are divided into three different benchmark groups in this study. A total of sixty-three low, medium, and large scale knapsack problems were determined as test datasets. The performances of BAO-T and BAO-CM algorithms were examined in detail and the results were clearly shown with graphics. In addition, the results of BAO-T and BAO-CM algorithms have been compared with the new heuristic algorithms proposed in the literature in recent years, and their success has been proven. According to the results, BAO-CM performed better than BAO-T and can be suggested as an alternative algorithm for solving binary optimization problems.
Citation - WoS: 44
Citation - Scopus: 47
Binary Artificial Algae Algorithm for Feature Selection
(Elsevier, 2022) Türkoğlu, Bahaeddin; Uymaz, Sait Ali; Kaya, Ersin
In this study, binary versions of the Artificial Algae Algorithm (AAA) are presented and employed to determine the ideal attribute subset for classification processes. AAA is a recently proposed algorithm inspired by microalgae's living behavior, which has not been consistently implemented to determine ideal attribute subset (feature selection) processes yet. AAA can effectively look into the feature space for ideal attributes combination minimizing a designed objective function. The proposed binary versions of AAA are employed to determine the ideal attribute combination that maximizes classification success while minimizing the count of attributes. The original AAA is utilized in these versions while its continuous spaces are restricted in a threshold using an appropriate threshold function after flattening them. In order to demonstrate the performance of the presented binary artificial algae algorithm model, an experimental study was conducted with the latest seven highperformance optimization algorithms. Several evaluation metrics are used to accurately evaluate and analyze the performance of these algorithms over twenty-five datasets with different difficulty levels from the UCI Machine Learning Repository. The experimental results and statistical tests verify the performance of the presented algorithms in increasing the classification accuracy compared to other state-of-the-art binary algorithms, which confirms the capability of the AAA algorithm in exploring the attribute space and deciding the most valuable features for classification problems. (C) 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 31
Citation - Scopus: 32
A Binary Artificial Bee Colony Algorithm and Its Performance Assessment
(PERGAMON-ELSEVIER SCIENCE LTD, 2021) Kıran, Mustafa Servet
Artificial bee colony algorithm, ABC for short, is a swarm-based optimization algorithm proposed for solving continuous optimization problems. Due to its simple but effective structure, some binary versions of the algorithm have been developed. In this study, we focus on modification of its xor-based binary version, called as binABC. The solution update rule of basic ABC is replaced with a xor logic gate in binABC algorithm, and binABC works on discretely-structured solution space. The rest of components in binABC are the same as with the basic ABC algorithm. In order to improve local search capability and convergence characteristics of binABC, a stigmergic behavior-based update rule for onlooker bees of binABC and extended version of xor-based update rule are proposed in the present study. The developed version of binABC is applied to solve a modern benchmark problem set (CEC2015). To validate the performance of proposed algorithm, a series of comparisons are conducted on this problem set. The proposed algorithm is first compared with the basic ABC and binABC on CEC2015 set. After its performance validation, six binary versions of ABC algorithm are considered for comparison of the algorithms, and a comprehensive comparison among the state-of-art variants of swarm intelligence or evolutionary computation algorithms is conducted on this set of functions. Finally, an uncapacitated facility location problem set, a pure binary optimization problem, is considered for the comparison of the proposed algorithm and binary variants of ABC algorithm. The experimental results and comparisons show that the proposed algorithm is successful and effective in solving binary optimization problems as its basic version in solving continuous optimization problems.
Citation - WoS: 20
Citation - Scopus: 24
A Binary Social Spider Algorithm for Continuous Optimization Task
(SPRINGER, 2020) Baş, Emine; Ülker, Erkan
The social spider algorithm (SSA) is a new heuristic algorithm created on spider behaviors. The original study of this algorithm was proposed to solve continuous problems. In this paper, the binary version of SSA (binary SSA) is introduced to solve binary problems. Currently, there is insufficient focus on the binary version of SSA in the literature. The main part of the binary version is at the transfer function. The transfer function is responsible for mapping continuous search space to discrete search space. In this study, four of the transfer functions divided into two families, S-shaped and V-shaped, are evaluated. Thus, four different variations of binary SSA are formed as binary SSA-Tanh, binary SSA-Sigm, binary SSA-MSigm and binary SSA-Arctan. Two different techniques (SimSSA and LogicSSA) are developed at the candidate solution production schema in binary SSA. SimSSA is used to measure similarities between two binary solutions. With SimSSA, binary SSA's ability to discover new points in search space has been increased. Thus, binary SSA is able to find global optimum instead of local optimums. LogicSSA which is inspired by the logic gates and a popular method in recent years has been used to avoid local minima traps. By these two techniques, the exploration and exploitation capabilities of binary SSA in the binary search space are improved. Eighteen unimodal and multimodal standard benchmark optimization functions are employed to evaluate variations of binary SSA. To select the best variations of binary SSA, a comparative study is presented. The Wilcoxon signed-rank test has applied to the experimental results of variations of binary SSA. Compared to well-known evolutionary and recently developed methods in the literature, the variations of binary SSA performance is quite good. In particular, binary SSA-Tanh and binary SSA-Arctan variations of binary SSA showed superior performance.
Citation - WoS: 32
Citation - Scopus: 35
A Binary Social Spider Algorithm for Uncapacitated Facility Location Problem
(PERGAMON-ELSEVIER SCIENCE LTD, 2020) Baş, Emine; Ülker, Erkan
In order to find efficient solutions to real complex world problems, computer sciences and especially heuristic algorithms are often used. Heuristic algorithms can give optimal solutions for large scale optimization problems in an acceptable period. Social Spider Algorithm (SSA), which is a heuristic algorithm created on spider behaviors are studied. The original study of this algorithm was proposed to solve continuous problems. In this paper, the binary version of the Social Spider Algorithm called Binary Social Spider Algorithm (BinSSA) is proposed for binary optimization problems. BinSSA is obtained from SSA, by transforming constant search space to binary search space with four transfer functions. Thus, BinSSA variations are created as BinSSA1, BinSSA2, BinSSA3, and BinSSA4. The study steps of the original SSA are re-updated for BinSSA. A random walking schema in SSA is replaced by a candidate solution schema in BinSSA. Two new methods (similarity measure and logic gate) are used in candidate solution production schema for increasing the exploration and exploitation capacity of BinSSA. The performance of both techniques on BinSSA is examined. BinSSA is named as BinSSA(Sim&Logic). Local search and global search performance of BinSSA is increased by these two methods. Three different studies are performed with BinSSA. In the first study, the performance of BinSSA is tested on the classic eighteen unimodal and multimodal benchmark functions. Thus, the best variation of BinSSA and BinSSA (Sim&Logic) is determined as BinSSA4(Sim&Logic). BinSSA4(Sim&Logic) has been compared with other heuristic algorithms on CEC2005 and CEC2015 functions. In the second study, the uncapacitated facility location problems (UFLPs) are solved with BinSSA(Sim&Logic). UFL problems are one of the pure binary optimization problems. BinSSA is tested on low-scaled, middle-scaled, and large-scaled fifteen UFLP samples and obtained results are compared with eighteen state-of-art algorithms. In the third study, we solved UFL problems on a different dataset named M* with BinSSA(Sim&Logic). The results of BinSSA (Sim&Logic) are compared with the Local Search (LS), Tabu Search (TS), and Improved Scatter Search (ISS) algorithms. Obtained results have shown that BinSSA offers quality and stable solutions. (c) 2020 Elsevier Ltd. All rights reserved.
Citation - WoS: 10
Citation - Scopus: 11
A Binary Sparrow Search Algorithm for Feature Selection on Classification of X-Ray Security Images
(Elsevier Ltd, 2024) Babalik, A.; Babadag, A.
In today's world, especially in public places, strict security measures are being implemented. Among these measures, the most common is the inspection of the contents of people's belongings, such as purses, knapsacks, and suitcases, through X-ray imaging to detect prohibited items. However, this process is typically performed manually by security personnel. It is an exhausting task that demands continuous attention and concentration, making it prone to errors. Additionally, the detection and classification of overlapping and occluded objects can be challenging. Therefore, automating this process can be highly beneficial for reducing errors and improving the overall efficiency. In this study, a framework consisting of three fundamental phases for the classification of prohibited objects was proposed. In the first phase, a deep neural network was trained using X-ray images to extract features. In the subsequent phase, features that best represent the object were selected. Feature selection helps eliminate redundant features, leading to the efficient use of memory, reduced computational costs, and improved classification accuracy owing to a decrease in the number of features. In the final phase, classification was performed using the selected features. In the first stage, a convolutional neural network model was utilized for feature extraction. In the second stage, the Sparrow Search Algorithm was binarized and proposed as the binISSA for feature selection. Feature selection was implemented using the proposed binISSA. In the final stage, classification was performed using the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms. The performances of the convolutional neural network and the proposed framework were compared. In addition, the performance of the proposed framework was compared with that of other state-of-the-art meta-heuristic algorithms. The proposed method increased the classification accuracy of the network from 0.9702 to 0.9763 using both the KNN and SVM (linear kernel) classifiers. The total number of features extracted using the deep neural network was 512. With the application of the proposed binISSA, average number of features were reduced to 25.33 using the KNN classifier and 32.70 using the SVM classifier. The results indicate a notable reduction in the extracted features from the convolutional neural network and an improvement in the classification accuracy. © 2024 Elsevier B.V.
Citation - WoS: 7
Citation - Scopus: 9
Bindmo: a New Binary Dwarf Mongoose Optimization Algorithm on Based Z-Shaped, U-Shaped, and Taper-Shaped Transfer Functions for Cec-2017 Benchmarks
(Springer Science and Business Media Deutschland GmbH, 2024) Baş, Emine
Intelligent swarm optimization algorithms have become increasingly common due to their success in solving real-world problems. Dwarf Mongoose Optimization (DMO) algorithm is a newly proposed intelligent swarm optimization algorithm in recent years. It was developed for continuous optimization problem solutions in its original paper. But real-world problems are not always problems that take continuously variable values. Real-world problems are often problems with discrete variables. Therefore, heuristic algorithms proposed for continuous optimization problems need to be updated to solve discrete optimization problems. In this study, DMO has been updated for binary optimization problems and the Binary DMO (BinDMO) algorithm has been proposed. In binary optimization, the search space consists of binary variable values. Transfer functions are often used in the conversion of continuous variable values to binary variable values. In this study, twelve different transfer functions were used (four Z-shaped, four U-shaped, and four Taper-shaped). Thus, twelve different BinDMO variations were obtained (BinDMO1, BinDMO2, …, BinDMO12). The achievements of BinDMO variations were tested on thirteen different unimodal and multimodal classical benchmark functions. The effectiveness of population sizes on the effectiveness of BinDMO was also investigated. When the results were examined, it was determined that the most successful BinDMO variation was BinDMO1 (with Z1-shaped transfer function). The most successful BinDMO variation was compared with three different binary heuristic algorithms selected from the literature (SO, PDO, and AFT) on CEC-2017 benchmark functions. According to the average results, BinDMO was the most successful binary heuristic algorithm. This has proven that BinDMO can be chosen as an alternative algorithm for binary optimization problems. © The Author(s) 2024.
Citation - WoS: 11
Citation - Scopus: 13
Bingso: Galactic Swarm Optimization Powered by Binary Artificial Algae Algorithm for Solving Uncapacitated Facility Location Problems
(Springer London Ltd, 2022) Kaya, Ersin
Population-based optimization methods are frequently used in solving real-world problems because they can solve complex problems in a reasonable time and at an acceptable level of accuracy. Many optimization methods in the literature are either directly used or their binary versions are adapted to solve binary optimization problems. One of the biggest challenges faced by both binary and continuous optimization methods is the balance of exploration and exploitation. This balance should be well established to reach the optimum solution. At this point, the galactic swarm optimization (GSO) framework, which uses traditional optimization methods, stands out. In this study, the binary galactic swarm optimization (BinGSO) approach using binary artificial algae algorithm as the main search algorithm in GSO is proposed. The performance of the proposed binary approach has been performed on uncapacitated facility location problems (UFLPs), which is a complex problem due to its NP-hard structure. The parameter analysis of the BinGSO method was performed using the 15 Cap problems. Then, the BinGSO method was compared with both traditional binary optimization methods and the state-of-the-art methods which are used on Cap problems. Finally, the performance of the BinGSO method on the M* problems was examined. The results of the proposed approach on the M* problem set were compared with the results of the state-of-the-art methods. The results of the evaluation process showed that the BinGSO method is more successful than other methods through its ability to establish the balance between exploration and exploitation in UFLPs.
Citation - WoS: 21
Citation - Scopus: 24
Boosting the Oversampling Methods Based on Differential Evolution Strategies for Imbalanced Learning
(Elsevier, 2021) Korkmaz, Sedat; Sahman, Mehmet Akif; Çınar, Ahmet Cevahir; Kaya, Ersin
The class imbalance problem is a challenging problem in the data mining area. To overcome the low classification performance related to imbalanced datasets, sampling strategies are used for balancing the datasets. Oversampling is a technique that increases the minority class samples in various proportions. In this work, these 16 different DE strategies are used for oversampling the imbalanced datasets for better classification. The main aim of this work is to determine the best strategy in terms of Area Under the receiver operating characteristic (ROC) Curve (AUC) and Geometric Mean (G-Mean) metrics. 44 imbalanced datasets are used in experiments. Support Vector Machines (SVM), k-Nearest Neighbor (kNN), and Decision Tree (DT) are used as a classifier in the experiments. The best results are produced by 6th Debohid Strategy (DSt6), 1th Debohid Strategy (DSt1), and 3th Debohid Strategy (DSt3) by using kNN, DT, and SVM classifiers, respectively. The obtained results outperform the 9 state-of-the-art oversampling methods in terms of AUC and G-Mean metrics (C) 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 24
Citation - Scopus: 34
Boundary Constrained Voxel Segmentation for 3d Point Clouds Using Local Geometric Differences
(PERGAMON-ELSEVIER SCIENCE LTD, 2020) Sağlam, Ali; Makineci, Hasan Bilgehan; Baykan, Nurdan Akhan; Baykan, Ömer Kaan
In 3D point cloud processing, the spatial continuity of points is convenient for segmenting point clouds obtained by 3D laser scanners, RGB-D cameras and LiDAR (light detection and ranging) systems in general. In real life, the surface features of both objects and structures give meaningful information enabling them to be identified and distinguished. Segmenting the points by using their local plane directions (normals), which are estimated by point neighborhoods, is a method that has been widely used in the literature. The angle difference between two nearby local normals allows for measurement of the continuity between the two planes. In real life, the surfaces of objects and structures are not simply planes. Surfaces can also be found in other forms, such as cylinders, smooth transitions and spheres. The proposed voxel-based method developed in this paper solves this problem by inspecting only the local curvatures with a new merging criteria and using a non-sequential region growing approach. The general prominent feature of the proposed method is that it mutually one-to-one pairs all of the adjoining boundary voxels between two adjacent segments to examine the curvatures of all of the pairwise connections. The proposed method uses only one parameter, except for the parameter of unit point group (voxel size), and it does not use a mid-level over-segmentation process, such as supervoxelization. The method checks the local surface curvatures using unit normals, which are close to the boundary between two growing adjacent segments. Another contribution of this paper is that some effective solutions are introduced for the noise units that do not have surface features. The method has been applied to one indoor and four outdoor datasets, and the visual and quantitative segmentation results have been presented. As quantitative measurements, the accuracy (based on the number of true segmented points over all points) and F1 score (based on the means of precision and recall values of the reference segments) are used. The results from testing over five datasets show that, according to both measurement techniques, the proposed method is the fastest and achieves the best mean scores among the methods tested. (C) 2020 Elsevier Ltd. All rights reserved.
Citation - WoS: 11
Citation - Scopus: 12
Chaotic Artificial Algae Algorithm for Solving Global Optimization With Real-World Space Trajectory Design Problems
(Springer Heidelberg, 2025) Turkoğlu, Bahaeddin; Uymaz, Sait Ali; Kaya, Ersin
The artificial algae algorithm (AAA) is a recently introduced metaheuristic algorithm inspired by the behavior and characteristics of microalgae. Like other metaheuristic algorithms, AAA faces challenges such as local optima and premature convergence. Various strategies to address these issues and enhance the performance of the algorithm have been proposed in the literature. These include levy flight, local search, variable search, intelligent search, multi-agent systems, and quantum behaviors. This paper introduces chaos theory as a strategy to improve AAA's performance. Chaotic maps are utilized to effectively balance exploration and exploitation, prevent premature convergence, and avoid local minima. Ten popular chaotic maps are employed to enhance AAA's performance, resulting in the chaotic artificial algae algorithm (CAAA). CAAA's performance is evaluated on thirty benchmark test functions, including unimodal, multimodal, and fixed dimension problems. The algorithm is also tested on three classical engineering problems and eight space trajectory design problems at the European Space Agency. A statistical analysis using the Friedman and Wilcoxon tests confirms that CAA demonstrates successful performance in optimization problems.
Citation - WoS: 17
Citation - Scopus: 15
Chaotic Golden Ratio Guided Local Search for Big Data Optimization
(Elsevier - Division Reed Elsevier India Pvt Ltd, 2023) Koçer, Havva Gül; Türkoğlu, Bahaeddin; Uymaz, Sait Ali
Biological systems where order arises from disorder inspires for many metaheuristic optimization techniques. Self-organization and evolution are the common behaviour of chaos and optimization algorithms. Chaos can be defined as an ordered state of disorder that is hypersensitive to initial conditions. Therefore, chaos can help create order out of disorder. In the scope of this work, Golden Ratio Guided Local Search method was improved with inspiration by chaos and named as Chaotic Golden Ratio Guided Local Search (CGRGLS). Chaos is used as a random number generator in the proposed method. The coefficient in the equation for determining adaptive step size was derived from the Singer Chaotic Map. Performance evaluation of the proposed method was done by using CGRGLS in the local search part of MLSHADE-SPA algorithm. The experimental studies carried out with the electroencephalographic signal decomposition based optimization problems, named as Big Data optimization problem (Big-Opt), introduced at the Congress on Evolutionary Computing Big Data Competition (CEC'2015). Experimental results have shown that the local search method developed using chaotic maps has an effect that increases the performance of the algorithm.& COPY; 2023 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Citation - WoS: 24
Citation - Scopus: 30
Classification of Physiological Disorders in Apples Fruit Using a Hybrid Model Based on Convolutional Neural Network and Machine Learning Methods
(Springer London Ltd, 2022) Büyükarıkan, Birkan; Ülker, Erkan
Physiological disorders in apples are due to post-harvest conditions. For this reason, automatic identification of physiological disorders is important in obtaining agricultural information. Image processing is one of the techniques that can help achieve the features of physiological disorders. Physiological disorders during image acquisition can be affected by the changes in brightness values created by different lighting conditions. This changes the results of the classification. In recent years, the convolutional neural network (CNN) has been a successful approach in automatically obtaining deep features from raw images in image classification problems. The study aims to classify physiological disorders using machine learning (ML) methods according to extracted deep features of the images under different lighting conditions. The data sets were created by acquired images (1080 images) and augmentation images (4320 images). Deep features were extracted using five popular pre-trained CNN models in these data sets, and these features were classified using five ML methods. The highest average accuracy was obtained with the VGG19(fc6) + SVM method in the data set-1 and data set-2 and were 96.11 and 96.09%, respectively. With this study, physiological disorders can be determined early, and needed precautions can be taken before and after harvest, not too late.
Citation - WoS: 3
Citation - Scopus: 3
Classification of Physiological Disorders in Apples Using Deep Convolutional Neural Network Under Different Lighting Conditions
(Springer, 2023) Büyükarıkan, Birkan; Ülker, Erkan
Non-destructive testing of apple fruit, an important product in the world fresh fruit trade, according to physiological disorders, can be done with a computer vision system. However, in the vision system, images may be affected by the brightness values created by different lighting conditions. For this reason, it is a necessity to use algorithms that accurately and quickly detect physiological disorders. By using a convolutional neural network (CNN), an algorithm that enables easy extraction of features from images, determining physiological disorders becomes easier. This study aims to classify the images of apples with physiological disorders obtained under different lighting conditions with CNN models. This study created a dataset (images of different light colors, angles, and distances) with some physiological disorder images. A 5-fold cross-validation method was applied to improve the generalization ability of the models, and CNN models were trained end-to-end. In addition, the Friedman hypothesis test and post-hoc Nemenyi test were performed to compare the evaluation indicators of different CNN models. The average accuracy, precision, recall, and F1-score of the Xception model were 0.996, 0.994, 0.996, and 0.998, respectively. The classification accuracy of this model is followed by the ResNet101, MobileNet, ResNet152, ResNet18, ResNet34, ResNet50, EfficientNetB0, AlexNet, VGG16, and VGG19. Finally, Xception performed well, according to Friedman/Nemenyi test results.
Citation - WoS: 8
Citation - Scopus: 9
A Comparative Study of Swarm Intelligence and Evolutionary Algorithms on Urban Land Readjustment Problem
(ELSEVIER, 2021) Koç, İsmail; Babaoğlu, İsmail
Land Readjustment and redistribution (LR) is a land management tool that helps regular urban development with the contribution of landowners. The main purpose of LR is to transform irregularly developed land parcels into suitable forms. Since it is necessary to handle many criteria simultaneously to solve LR problems, classical mathematical methods can be insufficient due to time limitation. Since LR problems are similar to traveling salesman problems and typical scheduling problems in terms of structure, they are kinds of NP-hard problems in combinatorial optimization. Therefore, metaheuristic algorithms are used in order to solve NP-hard problems instead of classical methods. At first, in this study, an effective problem-specific objective function is proposed to address the main criteria of the problem. In addition, a map-based crossover operator and three different mutation operators are proposed for the LR, and then a hybrid approach is implemented by utilizing those operators together. Furthermore, since the optimal value of the problem handled in real world cannot be exactly estimated, a synthetic dataset is proposed as a benchmarking set in LR which makes the success of algorithms can be objectively evaluated. This dataset consists of 5 different problems according to number of parcel which are 20, 40, 60, 80 and 100. Each problem set consists of 4 sub-problems in terms of number of landowners per-parcel which are 1, 2, 3 and 4. Therefore, the dataset consists of 20 kinds of problems. In this study, artificial bee colony, particle swarm optimization, differential evolution, genetic and tree seed algorithm are used. In the experimental studies, five algorithms are set to run under equal conditions using the proposed synthetic dataset. When the acquired experimental results are examined, genetic algorithm seems to be the most effective algorithm in terms of both speed and performance. Although artificial bee colony has better results from genetic algorithm in a few problems, artificial bee colony is the second most successful algorithm after genetic algorithm in terms of performance. However, in terms of time, artificial bee colony is an algorithm nearly as successful as genetic algorithm. On the other hand, the results of differential evolution, particle swarm optimization and tree seed algorithms are similar to each other in terms of solution quality. In conclusion, the statistical tests clearly show that genetic algorithm is the most effective technique in solving LR problems in terms of speed, performance and robustness. (C) 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 4
Comparing the Performances of Six Nature-Inspired Algorithms on a Real-World Discrete Optimization Problem
(Springer, 2022) Haklı, Hüseyin; Uguz, Harun; Ortacay, Zeynep
Many new, nature-inspired optimization algorithms are proposed these days, and these algorithms are gaining popularity day by day. These algorithms are frequently preferred for these real-world problems as they need less information, are reliable and robust, and have a structure that can easily be applied to discrete problems. Too many algorithms result in difficulty choosing the correct technique for the problem, and selecting an unwise method affects the solution quality. In addition, some algorithms cannot be reliable for some specific real-world problems but very successful for others. In order to guide and give insight into the practitioners and researchers about this problem, studies involving the comparison and evaluation of the performance of algorithms are needed. In this study, the performances of six nature-inspired methods, which included five new implementations of differential evolutionary algorithms (DE), scatter search (SS), equilibrium optimizer (EO), marine predators algorithm (MPA), and honey badger algorithm (HBA) applied to land redistribution problem and genetic algorithms (GA), were compared. In order to compare the algorithms in detail, various performance indicators were used as problem based and algorithm based. Experimental results showed that DE and SS algorithms have a more successful performance than the other methods by solution quality, robustness, and many problem-based indicators.
Citation - WoS: 1
Citation - Scopus: 8
Comparison Between Ssa and Sso Algorithm Inspired in the Behavior of the Social Spider for Constrained Optimization
(SPRINGER, 2021) Baş, Emine; Ülker, Erkan
The heuristic algorithms are often used to find solutions to real complex world problems. In this paper, the Social Spider Algorithm (SSA) and Social Spider Optimization (SSO) which are heuristic algorithms created upon spider behaviors are considered. Performances of both algorithms are compared with each other from six different items. These are; fitness values of spider population which are obtained in different dimensions, number of candidate solution obtained in each iteration, the best value of candidate solutions obtained in each iteration, the worst value of candidate solutions obtained in each iteration, average fitness value of candidate solutions obtained in each iteration and running time of each iteration. Obtained results of SSA and SSO are applied to the Wilcoxon signed-rank test. Various unimodal, multimodal, and hybrid standard benchmark functions are studied to compare each other with the performance of SSO and SSA. Using these benchmark functions, performances of SSO and SSA are compared with well-known evolutionary and recently developed methods in the literature. Obtained results show that both heuristic algorithms have advantages to another from different aspects. Also, according to other algorithms have good performance.
Citation - WoS: 20
Citation - Scopus: 18
Comparison of Different Optimization Based Land Reallocation Models
(ELSEVIER SCI LTD, 2020) Uyan, Mevlüt; Tongur, Vahit; Ertunç, Ela
Land reallocation, which is an optimization problem in the field of engineering, is the process of reallocating parcels to pre-determined blocks according to the preferences of landowners. In practice, this is done manually and takes weeks or even months. The elongation of this process affects both the cost of the project and the project's acceptability by the landowners and thus the success of the project. Because the success of land consolidation projects is determined by the satisfaction of the landowners. For these reasons, the optimization-based land reallocation studies have been extensively carried out recently. However, these methods in the literature are not used in practice and the reallocation is still done manually. Therefore, for the first time in this study, two new reallocation models were developed to solve this problem by using Migration Birds and Simulated Annealing Algorithms and the results of these methods in a real project area were compared. Additionally, the results were compared to the conventional reallocation method (manual reallocation) to evaluate the performance of the methods developed. Both proposed methods provided a successful and practicable reallocation plan in a very short time with respect to the conventional one.
Citation - WoS: 15
Citation - Scopus: 19
A Comprehensive Analysis of Grid-Based Wind Turbine Layout Using an Efficient Binary Invasive Weed Optimization Algorithm With Levy Flight
(Pergamon-Elsevier Science Ltd, 2022) Koç, İsmail
Wind energy has attracted great attention in recent years due to the increasing demand for alternative energy sources. Gathering the maximum amount of energy from wind energy is directly related to the layout of wind turbines in wind farm. This study focuses on grid-based wind turbine layout problem in an area of 2 km x 2 km. In order to solve this problem, 9 novel grids of 11 x 11, 12 x 12, ... and 19 x 19 are proposed in this paper in addition to 10 x 10 and 20 x 20 turbine grids which have already used in the literature. A new repair operator is recommended, taking into account the distance constraint between two adjacent cells in turbine layout problem, and thus the obtained solutions are made feasible. Furthermore, a levy flight-based IWO (LFIWO) algorithm is developed to optimize the layout of the turbines in wind farm. The basic IWO algorithm and the proposed LFIWO algorithm are compared on 11 different turbine layouts. Experimental studies are carried out for both algorithms under equal conditions with the help of 10 different binary versions. According to the comparisons performed using 11 different grids, LFIWO demonstrates a much superior success than the IWO algorithm. In addition, the proposed 19 x 19 grid reveals the best success among the other grids. When compared to the other studies in the literature, it is seen that LFIWO surpasses the other algorithms in terms of solution quality. As a result, it can be clearly stated that the proposed binary version of LFIWO is a competitive and effective binary algorithm.

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