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    The Analysis of Migrating Birds Optimization Algorithm with Neighborhood Operator on Traveling Salesman Problem
    (Springer International Publishing Ag, 2016) Tongur, Vahit; Ulker, Erkan
    Migrating 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.
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    An approach for the probability of wedge failure in the excavation direction
    (Crc Press-Balkema, 2018) Turanboy, Alparslan; Ulker, Erkan; Kucuksutcu, Cahit Burak
    This paper aims to show the results of a methodology used in the analysis of rock slopes in terms of wedge failures based on assumed new surfaces in the excavation direction that links a well-developed database structure, visual representations, basic limit equilibrium analysis and statistical analyses. The method presented here is intended to clarify the complexity of the structure of rock slopes that include wedge blocks in the first step of the model. The structural data analyses used here consist of a series of sorting and filtering processes for which the raw data are derived from scan-line surveys in this step. In the second step, visual representations, spatial variability, size distributions. The last step of the developed model includes Monte Carlo simulation (MCs), which is devoted to assessing the instabilities of rock slopes based on actual and planned new excavation surfaces, which are named the Hypothetical Excavation Surfaces (HES) of the rock slopes. The developed model has been tested on a highway slope as a field experiment. The experimental slope has seemed to be unstable during an assumed excavation according to the results of the analyses.
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    B-Spline Curve Knot Estimation by Using Niched Pareto Genetic Algorithm (NPGA)
    (Springer International Publishing Ag, 2016) Tongur, Vahit; Ulker, Erkan
    In 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.
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    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, Erkan
    In 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.
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    Migrating Birds Optimization (MBO) Algorithm to Solve 0-1 Multidimensional Knapsack Problem
    (IEEE, 2017) Tongur, Vahit; Ulker, Erkan
    This 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.
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    Migrating birds optimization (MBO) algorithm to solve knapsack problem
    (Elsevier Science Bv, 2017) Ulker, Erkan; Tongur, Vahit
    This 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.
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    PSO-based improved multi-flocks migrating birds optimization (IMFMBO) algorithm for solution of discrete problems
    (Springer, 2019) Tongur, Vahit; Ulker, Erkan
    In 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.
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    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, Erkan
    The 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.