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    Analysis of different binarization techniques within whale optimization algorithm
    (IEEE, 2019) Gölcük, İlker; Özsoydan, Fehmi Burçin; Durmaz, Esra Duygu
    In this study, Whale Optimization Algorithm (WOA), which is a recently proposed swarm intelligence-based algorithm, has been used to solve binary optimization problems. As the WOA algorithm has been developed for optimizing realvalued functions, binary versions of the WOA algorithm have been adopted for the binary optimization problems. The performance of modulation-, normalization-, s-shaped transfer function-, and angle modulation-based binarization approaches are compared. The proposed algorithms tested on the well-known benchmark problems, namely one-max, plateau, deceptive, and royal road. Our computational experiments show that angle modulation and normalization-based binarization approaches give the best results, and binary WOA is promising and has potential to handle difficult binary optimization problems.
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    An improved arithmetic optimization algorithm for training feedforward neural networks under dynamic environments
    (Elsevier, 2023) Gölcük, İlker; Özsoydan, Fehmi Burçin; Durmaz, Esra Duygu
    This paper proposes an improved Arithmetic Optimization Algorithm (AOA) to train artificial neural networks (ANNs) under dynamic environments. Despite many successful applications of metaheuristic training of ANNs, these studies assume static environments, which might not be realistic in real-world nonstationary processes. In this study, the training of ANNs is modeled as a dynamic optimization problem, and the proposed AOA is used to optimize connection weights and biases of the ANN under the presence of concept drift. The proposed method is designed to work for classification tasks. The performance of the proposed algorithm has been tested on twelve dynamic classification problems. Comparative analysis with state-of-the-art metaheuristic optimization algorithms has been provided. The superiority of the compared algorithms has been verified using nonparametric statistical tests. The results show that the improved AOA outperforms compared algorithms in training ANNs under dynamic environments. The findings demonstrate the potential of improved AOA for dynamic data-driven applications.(c) 2023 Elsevier B.V. All rights reserved.
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    Interval type-2 fuzzy development of FUCOM and activity relationship charts along with MARCOS for facilities layout evaluation
    (Elsevier, 2022) Gölcük, İlker; Durmaz, Esra Duygu; Şahin, Ramazan
    This paper presents a new interval type-2 fuzzy (IT2F) multiple attribute decision making (MADM) model for evaluating facility layout alternatives. To this end, The Full Consistency Method (FUCOM) is extended to IT2F sets, and the mathematical model of IT2F-FUCOM is proposed. IT2F-FUCOM method requires a manageable number of pairwise comparisons and can deal with imprecision and uncertainty. Furthermore, the Activity Relationship Charts (ARCs), which are powerful instruments to articulate preferences regarding closeness between departments in the facility design, are also modeled through IT2F sets. The proposed IT2F-ARCs are incorporated into the decision hierarchy as a cost-type criterion, a novel integration between ARCs and the MADM is provided. Finally, the state-of-the-art IT2F MADM method of Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) is utilized to rank the alternatives. A real-life case study is conducted in order to demonstrate the applicability of the proposed model. (C) 2022 Elsevier B.V. All rights reserved.
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    Prioritizing occupational safety risks with fuzzy FUCOM and fuzzy graph theory-matrix approach
    (Gazi Univ, Fac Engineering Architecture, 2023) Gölcük, İlker; Durmaz, Esra Duygu; Şahin, Ramazan
    In this study, a new failure mode and effects analysis (FMEA) model is proposed for evaluating occupational safety risks. In the classical FMEA, risk priority numbers (RPNs) are calculated by multiplying the risk scores of the occurrence, severity, and detectability. However, RPN numbers generated by classical FMEA have been the subject of severe criticism in the literature. To overcome the drawbacks of the classical FMEA, this study proposes a new Multiple Attribute Decision Making (MADM) model. The proposed risk evaluation model combines the full consistency method (FUCOM) and graph theory-matrix approach (GTMA) under a fuzzy environment. The risk scores of failure models and the weights of risk factors have been obtained using the fuzzy FUCOM method. On the other hand, the RPN value of each failure mode is calculated by utilizing fuzzy GTMA. Fuzzy GTMA considers all possible dependencies among risk factors, which in turn produces more accurate rankings. The fuzzy judgements of the decision makers are aggregated by using the least squares distance method. The proposed model is implemented in a real-life case study and the failure modes are ranked.