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Öğe The Analysis of Migrating Birds Optimization Algorithm with Neighborhood Operator on Traveling Salesman Problem(SPRINGER INTERNATIONAL PUBLISHING AG, 2016) Tongur, Vahit; Ulker, ErkanMigrating birds optimization (MBO) algorithm is a new meta-heuristic algorithm inspired from behaviors of migratory birds during migration. Basic MBO algorithm is designed for quadratic assignment problems (QAP) which are known as discrete problems, and the performance of MBO algorithm for solving QAP is shown successfully. But MBO algorithm could not achieve same performance for some other benchmark problems like traveling salesman problem (TSP) and asymmetric traveling salesman problem (ATSP). In order to deal with these kinds of problems, neighborhood operators of MBO is focused in this paper. The performance of MBO algorithm is evaluated with seven varieties of neighborhood operators on symmetric and asymmetric TSP problems. Experimental results show that the performance of MBO algorithm is improved up to 36% by utilizing different neighborhood operators.Öğe The application of ant colony optimization in the solution of 3D traveling salesman problem on a sphere(ELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD, 2017) Eldem, Huseyin; Ulker, ErkanTraveling Salesman Problem (TSP) is a problem in combinatorial optimization that should be solved by a salesperson who has to travel all cities at the minimum cost (minimum route) and return to the starting city (node). Todays, to resolve the minimum cost of this problem, many optimization algorithms have been used. The major ones are these metaheuristic algorithms. In this study, one of the metaheuristic methods, Ant Colony Optimization (ACO) method (Max-Min Ant System - MMAS), was used to solve the Non-Euclidean TSP, which consisted of sets of different count points coincidentally located on the surface of a sphere. In this study seven point sets were used which have different point count. The performance of the MMAS method solving Non-Euclidean TSP problem was demonstrated by different experiments. Also, the results produced by ACO are compared with Discrete Cuckoo Search Algorithm (DCS) and Genetic Algorithm (GA) that are in the literature. The experiments for TSP on a sphere, show that ACO's average results were better than the GA's average results and also best results of ACO successful than the DCS. (C) 2017 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND licenseÖğe An artificial immune system approach for B-spline surface approximation problem(SPRINGER-VERLAG BERLIN, 2007) Ulker, Erkan; Isler, VeysiIn surface fitting problems, the selection of knots in order to get an optimized surface for a shape design is well-known. For large data, this problem needs to be dealt with optimization algorithms avoiding possible local optima and at the same time getting to the desired solution in an iterative fashion. Many computational intelligence optimization techniques like evolutionary optimization algorithms, artificial neural networks and fuzzy logic have already been successfully applied to the problem. This paper presents an application of another computational intelligence technique known as "Artificial Immune Systems (AIS)" to the surface fitting problem based on B-Splines. Our method can determine appropriate number and locations of knots automatically and simultaneously. Numerical examples are given to show the effectiveness of our method. Additionally, a comparison between the proposed method and genetic algorithm is presented.Öğe B-spline curve fitting with invasive weed optimization(ELSEVIER SCIENCE INC, 2017) Uyar, Kubra; Ulker, ErkanB-spline curves and surfaces are generally used in computer aided design (CAD), data visualization, virtual reality, surface modeling and many other fields. Especially, data fitting with B-splines is a challenging problem in reverse engineering. In addition to this, B-splines are the most preferred approximating curve because they are very flexible and have powerful mathematical properties and, can represent a large variety of shapes efficiently [1]. The selection of the knots in B-spline approximation has an important and considerable effect on the behavior of the final approximation. Recently, in literature, there has been a considerable attention paid to employing algorithms inspired by natural processes or events to solve optimization problems such as genetic algorithms, simulated annealing, ant colony optimization and particle swarm optimization. Invasive weed optimization (IWO) is a novel optimization method inspired from ecological events and is a phenomenon used in agriculture. In this paper, optimal knots are selected for B-spline curve fitting through invasive weed optimization method. Test functions which are selected from the literature are used to measure performance. Results are compared with other approaches used in B-spline curve fitting such as Lasso, particle swarm optimization, the improved clustering algorithm, genetic algorithms and artificial immune system. The experimental results illustrate that results from IWO are generally better than results from other methods. (C) 2017 Elsevier Inc. All rights reserved.Öğe B-Spline Curve Knot Estimation by Using Niched Pareto Genetic Algorithm (NPGA)(SPRINGER INTERNATIONAL PUBLISHING AG, 2016) Tongur, Vahit; Ulker, ErkanIn this paper, estimated curve Knot points are found for B-Spline Curve by using Niched (Celled) Pareto Genetic Algorithm which is one of the multi objective genetic algorithms. It is necessary to know degree of the curve, control points and knot vector for drawing B-Spline curve. Some knot points are of very few or no effect at all on the drawing of B-Spline curve drawing. Omitting such points will not effect the shape of curve in curve drawing. In this study, it is aimed to find and omit these ineffective curve points from drove of curve. Performance of proposed method are compared with selected studies from literature.Öğe A binary social spider algorithm for continuous optimization task(SPRINGER, 2019) Bas, Emine; Ulker, ErkanThe 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.Öğe The calculation of parametric NURBS surface interval values using neural networks(SPRINGER-VERLAG BERLIN, 2006) Ulker, Erkan; Arslan, AhmetThree dimensional coordinate values of parametric NURBS (NonUniform Rational B-Splines) surfaces are obtained from two dimensional parameters u and v. An approach for generating surfaces produces a model by giving a fixed increase to u and v values. However, the ratio of three dimensional parameters increases and fixed increase of u and v values is not always the same. This difference of ratio costs unrequited sized breaks. In this study an artificial neural network method for simulation of a NURBS surface is proposed. Free shaped NURBS surfaces and various three dimensional object simulations with different patches can be produced using a method projected as network training with respect to coordinates which are found from interval scaled parameters. Experimental results show that this method in imaging modeled surface can be used as a simulator.Öğe A color image watermarking scheme based on artificial immune recognition system(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Findik, Oguz; Babaoglu, Ismail; Ulker, ErkanThis study suggests a novel watermarking technique that uses artificial immune recognition system to protect color image's intellectual property rights. The watermark is embedded in the blue channel of a color image. m-bit binary sequence embedded into the color image is used to train artificial immune recognition system. With this composed technique, extracting the watermark which is embedded into the color image is carried out using artificial immune recognition system. It is observed that the composed technique achieves high performance to process of extracting this watermark. The watermark is extracted successfully from the watermarked image after various image processing attacks as well. (C) 2010 Elsevier Ltd. All rights reserved.Öğe IMPLEMENTATION OF BCH CODING ON ARTIFICIAL NEURAL NETWORK-BASED COLOR IMAGE WATERMARKING(ICIC INTERNATIONAL, 2011) Findik, Oguz; Babaoglu, Ismail; Ulker, ErkanThis study suggests a novel watermarking technique that uses artificial neural networks (ANN) and BCH (Bose, Chaudhuri and Hocquenghem) coding together to protect intellectual property rights of a color image. BCH error correction coding method is used to improve the performance of watermark extracting. With this composed technique, image is divided into sub-blocks, and a bit-sequence which is used to train both ANN and the watermark is added to the selected sub-blocks. In the watermark embedding process, besides embedding the bit-sequence as is, the watermark is embedded by encoding the watermark into the original image through BCH coding method. ANN is trained by using the features obtained from the selected sub-blocks to which the bit-sequence is embedded. The extraction process is implemented by using the trained ANN and the features obtained from the selected sub-blocks to which the encoded watermark is embedded. After the extraction process, the extracted watermark is obtained by using BCH decoding method. The results of the study are obtained by using the watermark as is and by encoding with BCH coding method. By using BCH encoding method, watermark extraction success is considerably increased, especially on the watermark extraction cases with low success rates. The watermark is extracted considerably successfully from the watermarked image after various image processing attacks as well.Öğe An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization(HINDAWI LTD, 2013) Celik, Yuksel; Ulker, ErkanMarriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.Öğe LIP-RM: an attempt at 3D visualization of in situ rock mass structures(SPRINGER, 2008) Turanboy, Alparslan; Ulker, ErkanMapping of rock mass structure is an important task required in many applications of mining and contracture engineering. This task has to be routinely performed using established techniques developed to provide consistent results under the wide range conditions. In this research, a new methodology was developed to achieve this task by only utilizing the basic measurement instruments which were compass-clinometer and tape meter. The method's logic was based on the alternatives of spatial position of discontinuities that are classified under four types according to the north. In the developed approach, the geometry of discontinuities was evaluated as the linear relationships. Besides, isometric presentation was preferred in developed 3D simulation software, which was named as "linear isometric projection of rock mass". Input data of the developed software were the discontinuity geometric features such as dip, dip direction, and spacing on the rocky outcrop in the form of an information system. The output was a simulation model consisting of the rock mass structure. The new software derived from the developed approaches was tested on an experimental road wall outcrop. Obtained results are very close to the situation observed in the field, and the developed software is user-friendly. In this paper, a description of the numerical model and current capabilities of the software are introduced.Öğe A MARRIAGE IN HONEY BEE OPTIMISATION APPROACH TO THE ASYMMETRIC TRAVELLING SALESMAN PROBLEM(ICIC INTERNATIONAL, 2012) Celik, Yuksel; Ulker, ErkanIn the travelling salesman problem (TSP), a travelling salesman completes a tour of "n" number of cities by stopping once in each city and completes the tour by returning to his starting point, while minimizing the distance and the cost. The asymmetric travelling salesman problem (ATSP) is the problem in which the cost of travel from city A to B is different from that from B to A. Marriage in Honey Bee Optimisation (MBO) is a meta-heuristic procedure inspired by the mating and insemination process of honey bees. In this study, we seek to use an MBO algorithm for an optimal solution to the ATSP problem, which has previously been solved by different methods. The results of the MBO algorithm for ATSP are compared with Genetic Algorithm (GA), another meta-heuristic method.Öğe Maximum volume cuboids for arbitrarily shaped in-situ rock blocks as determined by discontinuity analysis-A genetic algorithm approach(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Ulker, Erkan; Turanboy, AlparslanThe block stone industry is one of the main commercial use of rock. The economic potential of any block quarry depends on the recovery rate, which is defined as the total volume of useful rough blocks extractable from a fixed rock volume in relation to the total volume of moved material. The natural fracture system, the rock type(s) and the extraction method used directly influence the recovery rate. The major aims of this study are to establish a theoretical framework for optimising the extraction process in marble quarries for a given fracture system, and for predicting the recovery rate of the excavated blocks. We have developed a new approach by taking into consideration only the fracture structure for maximum block recovery in block quarries. The complete model uses a linear approach based on basic geometric features of discontinuities for 3D models, a tree structure (TS) for individual investigation and finally a genetic algorithm (GA) for the obtained cuboid volume(s). We tested our new model in a selected marble quarry in the town of iscehisar (AFYONKARAHISAR-TURKEY). (C) 2009 Elsevier Ltd. All rights reserved.Öğe Migrating Birds Optimization (MBO) Algorithm to Solve 0-1 Multidimensional Knapsack Problem(IEEE, 2017) Tongur, Vahit; Ulker, ErkanThis study presents Migrating Birds Optimization (MBO) which is a novel meta-heuristic algorithm for the solution of 0-1 multidimensional knapsack problem. In the study, the basic migrating birds optimization algorithm is used and change is made to the only neighborhood structure of this algorithm for adapting to the addressed problem. The performance of the algorithm is examined on the test problems that selected from OR-library. The obtained results show that the migrating birds optimization algorithm has achieved successful results in 0-1 multidimensional backpack problems.Öğe Migrating birds optimization (MBO) algorithm to solve knapsack problem(ELSEVIER SCIENCE BV, 2017) Ulker, Erkan; Tongur, VahitThis study presents Migrating Birds Optimization (MBO) which is a novel meta-heuristic algorithm for the solution of knapsack problem. The knapsack problem which is classified as NP-complete problem is a combinatorial optimization problem. Its aim is to achieve maximum benefit without exceeding the capacity of the knapsack with selected item. The Migrating Birds Algorithm is designed for discrete problems. Therefore, the performance of basic the MBO algorithm is tested on the some knapsack problems and obtained results are demonstrated in detail. (C) 2017 The Authors. Published by Elsevier B.V.Öğe A NOVEL HYBRID CLASSIFICATION METHOD WITH PARTICLE SWARM OPTIMIZATION AND K-NEAREST NEIGHBOR ALGORITHM FOR DIAGNOSIS OF CORONARY ARTERY DISEASE USING EXERCISE STRESS TEST DATA(ICIC INTERNATIONAL, 2012) Babaoglu, Ismail; Findik, Oguz; Ulker, Erkan; Aygul, NazifThe aim of this study is to investigate the effectiveness of a novel hybrid method, particle swarm optimization with k-nearest neighbor classifier (PSOkNN), on determination of coronary artery disease (CAD) existence upon exercise stress testing (EST) data. The PSOkNN method is composed of two steps. At the first step, one particle which demonstrates the whole samples optimally in training dataset is generated for both healthy and unhealthy patients. Then, at the second one, the class of the test sample is determined according to the distance of the test sample to the generated particles utilizing k-nearest neighbor algorithm. To demonstrate the effectiveness of this novel method, the results of PSOkNN are compared with the classification results of the artificial immune recognition system and k-nearest neighbor algorithm. Besides, reliability of the proposed method on determination of CAD existence upon EST data is examined by using classification accuracy, k-fold cross-validation method and Cohen's kappa coefficient.
