Yazar "Atay, Yilmaz" seçeneğine göre listele

Listeleniyor 1 - 5 / 5
  • Küçük Resim Yok
    Öğe
    Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms
    (ELSEVIER, 2017) Atay, Yilmaz; Koc, Ismail; Babaoglu, Ismail; Kodaz, Halife
    In order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big BangBig Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks - five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested. (C) 2016 Elsevier B.V. All rights reserved.
  • Küçük Resim Yok
    Öğe
    Modularity-Based Graph Clustering using Harmony Search Algorithm
    (IEEE, 2015) Atay, Yilmaz; Kodaz, Halife
    Real-world networks contain variety of meaningful information inside them that can be revealed. These networks can be biological, social, ecological and technological networks. Each of these contains specific information about their field. This information cannot be obtained with simple techniques. Various techniques and algorithms have been developed to uncover useful information from complex relationships inside the network. In this paper, to divide graphs according to modularity measure to subgraphs harmony search algorithm is used which is inspired by music improvisation. This algorithm has been tested with 5 different real-world networks. The obtained quantitative values for each network have been given in the tables. In addition the proposed algorithm, has achieved the best known modularity measure of Zachary's Karate Club network which is commonly used in the literature and the latest subsets generated according to this modularity measure has been given at the end of section V. According to the results obtained from experiments it has been observed that HM algorithm gives faster results on solution of problem addressed in this study than most algorithms like genetic algorithm and bat algorithm. However, the proposed algorithm requires a larger size of harmony memory and more number of iterations for maximum modularity values.
  • Küçük Resim Yok
    Öğe
    A New Adaptive Genetic Algorithm for Community Structure Detection
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2016) Atay, Yilmaz; Kodaz, Halife
    Community structures exist in networks which has complex biological, social, technological and so on structures and contain important information. Networks and community structures in computer systems are presented by graphs and subgraphs respectively. Community structure detection problem is NP-hard problem and especially final results of the best community structures for large-complex networks are unknown. In this paper, to solve community structure detection problem a genetic algorithm-based algorithm, AGA-net, which is one of evolutionary techniques has been proposed. This algorithm which has the property of fast convergence to global best value without being trapped to local optimum has been supported by new parameters. Real-world network which are frequently used in literature has been used as test data and obtained results have been compared with 10 different algorithms. After analyzing the test results it has been observed that the proposed algorithm gives successful results for determination of meaningful communities from complex networks.
  • Küçük Resim Yok
    Öğe
    Optimization of job shop scheduling problems using modified clonal selection algorithm
    (TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2014) Atay, Yilmaz; Kodaz, Halife
    Artificial immune systems (AISs) are one of the artificial intelligence techniques studied a lot in recent years. AISs are based on the principles and mechanisms of the natural immune system. In this study, the clonal selection algorithm, which is used commonly in AISs, is modified. This algorithm is applied to job shop scheduling problems, which are one of the most difficult optimization problems. For applying application results to the optimum solution, parameter values giving the optimum solution are determined by analyzing the parameters in the algorithm. The obtained results are given in detail in the tables and figures. The best makespan values are reached in 7 out of 10 test problems (FT06, LA01, LA02, LA03, LA04, LA05, and ABZ6). Reasonable makespan values are reached for the remaining 3 problems (FT10, LA16, and ABZ5). The obtained results demonstrate that the developed system can be applied successfully to job shop scheduling problems.
  • Küçük Resim Yok
    Öğe
    A swarm intelligence-based hybrid approach for identifying network modules
    (ELSEVIER, 2018) Atay, Yilmaz; Aslan, Murat; Kodaz, Halife
    Complex network structures, where real-world systems are modelled, contain important information that can be uncovered. Various studies have been carried out, and many methods have been proposed recently to discover such information by using different network analysis techniques. The discovery of meaningful modules in networks is one of these significant works. In this study, a new hybrid method, which is called uniSFLA, is proposed to determine statistically significant modules within the network. Another significant aspect of this study is to use various objective functions as fitness criteria and compare the results obtained from the tests with each other. The aim is to test the success of various objective functions used to investigate network modules and those defined according to different properties in graphs. The proposed algorithm was tested on real-world networks, and the test results were compared with those of other algorithms from published literature. Considering the experimental results, the method suggested in this work produced significant success in terms of both best and average values. Moreover, the accuracy and quality tests of the conformity values obtained for each objective function were performed with four different cluster evaluation criteria. Finally, in addition to the successful results for the uniSFLA algorithm, the comparative test results of appropriate network modules, obtained using modularity and significance functions, were evaluated by means of various tables and graphs. (C) 2017 Elsevier B.V. All rights reserved.