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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 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 Comparative Analysis of Metaheuristic Approaches for Multidimensional Two-Way Number Partitioning Problem(Springer Heidelberg, 2018) Hacibeyoglu, Mehmet; Alaykiran, Kemal; Acilar, Ayse Merve; Tongur, Vahit; Ulker, ErkanIn this study, a novel usage of four metaheuristic approaches Genetic algorithm (GA), Simulated annealing (SA), migrating bird optimization algorithm (MBO) and clonal selection algorithm (CSA) are applied to multidimensional two-way number partitioning problem (MDTWNPP). MDTWNPP is a classical combinatorial NP-hard optimization problem where a set of vectors have more than one coordinate is partitioned into two subsets. The main objective function of MDTWNPP is to minimize the maximum absolute difference between the sums per coordinate of elements. In order to solve this problem, GA is applied with greedy crossover and mutation operators. SA is improved with dual local search mechanism. MBO is specialized as multiple flock migrating birds optimization algorithms. CSA is applied with problem specific hyper mutation process. Furthermore, all instances are solved using an integer linear programming model which was previously presented in the literature. In the experiments, four metaheuristic approaches and integer linear programming model are used to solve 126 datasets with different sizes and coordinates. As a brief result, the GA and SA approaches designed for this problem outperformed all other heuristics and the integer programming model. Both the performance of GA and SA approaches are in a competitive manner where GA and SA yielded the best solution for 56 and 65 out of 125 datasets, respectively.Öğe Comparison of different optimization based land reallocation models(Elsevier Sci Ltd, 2020) Uyan, Mevlut; Tongur, Vahit; Ertunc, ElaLand 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.Öğe Land reallocation model with simulated annealing algorithm(Taylor & Francis Ltd, 2021) Ertunc, Ela; Uyan, Mevlut; Tongur, VahitLand consolidation project has many stages. Land reallocation is the most considerable stage in which many factors play a role and forms the basis of this project. In this study, a new optimisation-based reallocation model has been developed to realise block reallocation by evaluating the requests of landowners. The reallocation according to the developed method also reset the block spaces automatically. The most powerful aspect of the method is that while the reallocation phase in land consolidation projects takes weeks and months, this method can be done in minutes. This method contributes to projects in terms of time and cost.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 PSO-based improved multi-flocks migrating birds optimization (IMFMBO) algorithm for solution of discrete problems(Springer, 2019) Tongur, Vahit; Ulker, ErkanIn this paper, we proposed an improved migrating birds optimization algorithm to solve discrete problem. It is a metaheuristic search algorithm that is inspired by V formation during the migration of migratory birds. Proposed algorithm has two main modifications on basic migrating birds algorithm. Firstly, multi-flocks are used instead of single flock in order to avoid local minimum. Secondly, these flocks interact with each other for the more detailed search around flock that has got better solutions. This interaction is inspired by particle swarm optimization algorithm. Also, insertion method is used for neighborhood in migrating birds optimization algorithm. As a discrete problem, traveling salesman problem is chosen. Performance of the proposed algorithm is tested on some of symmetric benchmark problems from TSPLIB. Obtained results show that proposed method is superior to basic migrating birds algorithm.Öğe Solving a big-scaled hospital facility layout problem with meta-heuristics algorithms(Elsevier - Division Reed Elsevier India Pvt Ltd, 2020) Tongur, Vahit; Hacibeyoglu, Mehmet; Ulker, ErkanThe main objective of the hospital facility layout problem is to place the polyclinics, laboratories and radiology units within the predefined boundaries in such way that minimize the movement cost of patients and healthcare staff. Especially in big-scaled hospitals including several different specialized departments, it is important in terms of hospital efficiency that interacting units are placed closely. Nowadays meta-heuristic algorithms are often used to solve optimization problems such as facility layout. In this study; polyclinic, laboratory and radiology units' layout of a big-scaled university hospital was organized using three meta-heuristic algorithms which are migrating bird optimization (MBO), tabu search (TS) and simulated annealing (SA). The results were compared with the existing clinic layout. Consequently MBO and SA meta-heuristic algorithms have given the same best results improving the existing clinic layout efficiency approximately by 58%. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.Öğe SOLVING THE BI-DIMENSIONAL TWO-WAY NUMBER PARTITIONING PROBLEM WITH HEURISTIC ALGORITHMS(IEEE, 2014) Hacibeyoglu, Mehmet; Tongur, Vahit; Alaykiran, KemalThe two-way number partitioning problem is to divide set of numbers into two subsets. As a result of the dividing process the sums of numbers in subsets must be as nearly equal as possible. The two-way number partitioning problem problem is NP-complete. The bi-dimensional two-way number partitioning problem is a kind of number partitioning problem. The sets have only two coordinates and the aim is minimized the differences of the sum of the numbers for both coordinates. This work presents two heuristic algorithm for solving bi-dimensional two-way number partitioning problem. Fist algorithm is best known and most used greedy algorithm. The other one is a novel genetic algorithm approach. These algorithms are analyzed, implemented and tested on randomly different 20 datasets.Öğe Use of the Migrating Birds Optimization (MBO) Algorithm in solving land distribution problem(Elsevier Sci Ltd, 2020) Tongur, Vahit; Ertunc, Ela; Uyan, MevlutLand distribution is an important process in Land Consolidation (LC) projects where agricultural parcels are reallocated to predetermined blocks. Land distribution is a process that takes a long time, requires high operating costs, and conflicts between landowners occur frequently. The parcels are tried to be placed in the best and most appropriate place of the existing blocks by considering many parameters in the distribution stage. Therefore, the placement of new parcels in blocks is seen as an optimization process. In LC projects, this process is carried out manually by technical staff using a software and thus it becomes a process that takes weeks and even months. Various methods have been developed to solve this important stage of the LC projects. It is required to find the best solution, since this issue is an optimization problem. This study aims to develop a new land distribution method. For this purpose, land distribution were carried out by use Migrating Birds Optimization (MBO) Algorithm. Used land distribution method in practice and the results of the new developed method were compared and thus the usability of the method that developed by us was tested. With this study, it has developed a new and successful distribution method according to the preference of land owners.
