Yazar "Ozkis, Ahmet" seçeneğine göre listele

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    A binary tree seed algorithm with selection-based local search mechanism for huge-sized optimization problems
    (Elsevier, 2022) Karakoyun, Murat; Ozkis, Ahmet
    Tree seed algorithm (TSA) is a recently proposed metaheuristic algorithm for solving continuous optimization problems. In order to use TSA in binary optimization problems, the SimLogicTSA method was developed by adding logic gates and Jaccard's similarity measure to this algorithm by Cinar and Kiran. Although SimLogicTSA is generally successful in small, medium, and large size problems, it has not been successful in the huge-sized problems by stucking into local minima. To overcome this problem, a new local search mechanism called enhanced local search module (ELSM) is proposed and the SimLogicTSA-ELSM algorithm is suggested by implementing the ELSM mechanism to the original SimLogicTSA algorithm. The proposed ELSM mechanism consists of a swap operator and logic-based gates. To analyze the contribution of the ELSM mechanism to the algorithm, firstly, the original SimLogicTSA and SimLogicTSA-ELSM algorithms were compared on the Cap and M* problem sets. The obtained results showed that the proposed algorithm produced more successful results than the original SimLogicTSA. Then, the proposed SimLogicTSA-ELSM is compared with many state-of -art algorithms in the literature by using different performance metrics on Cap and M* problem sets. The results show that SimLogicTSA-ELSM outperforms the compared algorithms in nearly all cases. Especially, the performance of the SimLogicTSA-ELSM stands out in huge-sized problems. (C) 2022 Elsevier B.V. All rights reserved.
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    Experimental and analytical investigation of chemical anchors's behaviour under axial tensile
    (Elsevier Sci Ltd, 2020) Musevitoglu, Abdullah; Arslan, Musa Hakan; Aksoylu, Ceyhun; Ozkis, Ahmet
    In this study, to observe the behavior of chemical anchors embedded in concrete under the tensile effect, 108 different anchor specimens were prepared with different parameters as concrete compressive strength, reinforcement bar diameters, anchor depths, sizes of drilled holes, cleanliness of the drilled holes. Pull-out tests were conducted and obtained data were examined with the axial-load capacities and the failure situations. Finally, the depth of anchors, compressive strength and reinforcement diameter were observed to increase the axial-load-bearing capacity. The specimens cleaned with water could bear more axial loads than cleaned using air. For the anchors installed without cleaning the holes, a significant decrease was observed in the axial-load carrying capacities compared to the other two conditions. The ANN algorithm exhibited a 78.3% prediction success compared with other algorithms. The empirical relations in the literature were found to have limited level of prediction success rates according to the ANN's results. (C) 2020 Elsevier Ltd. All rights reserved.
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    A new algorithm based on gray wolf optimizer and shuffled frog leaping algorithm to solve the multi-objective optimization problems
    (Elsevier, 2020) Karakoyun, Murat; Ozkis, Ahmet; Kodaz, Halife
    Multi-objective optimization is many important since most of the real world problems are in multiobjective category. Looking at the literature, the algorithms proposed for the solution of multi-objective problems have increased in recent years, but there is no a convenient approach for all kind of problems. Therefore, researchers aim to contribute to the literature by offering new approaches. In this study, an algorithm based on gray wolf optimizer (GWO) with memeplex structure of the shuffled frog leaping algorithm (SFLA), which is named as multi-objective shuffled GWO (MOSG), is proposed to solve the multi-objective optimization problems. Additionally, some modifications are applied on the proposed algorithm to improve the performance from different angles. The performance of the proposed algorithm is compared with the performance of six multi-objective algorithms on a benchmark set consist of 36 problems. The experimental results are presented with four different comparison metrics and statistical tests. According to the results, it can easily be said that the proposed algorithm is generally successful to solve the multi-objective problems and has better or competitive results. (C) 2020 Elsevier B.V. All rights reserved.
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    A new model based on vortex search algorithm for estimating energy demand of Turkey
    (Pamukkale Univ, 2020) Ozkis, Ahmet
    In this study, a new linear regression model based on Vortex Search (VS) algorithm was developed for estimating Turkey's energy demand. In this model, Turkey's gross domestic product (GDP), population, import and export data refer to input parameters; resulting energy demand refers to output to be estimated. Two different estimation models developed by using data between 1979-2005 and 1979-2011 were compared with similar studies in the literature. The results showed that the developed VS models obtained more successful or competitive results than the compared models. Finally in this study, the amount of energy that will be demanded in Turkey until 2030 was projected with VS and other models according to 3 different scenarios.