Browsing by Subject "Objective functions"

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    Fast global fuzzy C-means clustering for ECG signal classification; [EKG i̇şaretlerini siniflamak için hizli global bulanik C-ortalama öbekleşme]
    (2010) Koçyiǧit Y.; Kiliç I.
    Fuzzy clustering plays an important role in solving problems in the areas of pattern recognition and fuzzy model identification. The Fuzzy C-Means algorithm is one of widely used algorithms. It is based on optimizing an objective function, being responsive to initial conditions; the algorithm usually leads to local minimum results. Aiming at above problem, the fast global Fuzzy C-Means clustering algorithm (FGFCM) has been proposed, which is an incremental approach to clustering, and does not depend on any initial conditions. The algorithm was applied on ECG signals to classification. ©2010 IEEE.
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    Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence
    (Springer, 2021) Khalilpourazari S.; Khalilpourazary S.; Özyüksel Çiftçioğlu A.; Weber G.-W.
    This paper suggests a novel robust formulation designed for optimizing the parameters of the turning process in an uncertain environment for the first time. The aim is to achieve the lowest energy consumption and highest precision. With this aim, the current paper considers uncertain parameters, objective functions, and constraints in the offered mathematical model. We proposed several uncertain models and validated the results in real-world case studies. In addition, several artificial intelligence-based solution techniques are designed to solve the complex nonlinear problem. We determined the most efficient solution approach by solving various test problems. Then, simulated several scenarios to demonstrate the robustness of our results. The results showed that the solutions provided by the offered model significantly reduce energy consumption in different setups. To ensure the reliability of the results, we carried out worst-case sensitivity analyses and found the most critical parameters. The results of the worst-case analyses indicated that the offered robust model is efficient and saves a significant amount of energy comparing to traditional models. It is shown that the provided solution by the presented robust formulation is reliable in all situations and results in the lowest energy and the best machining precision. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
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    Solution of Process Synthesis Problem Using Metaheuristic Optimization Algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2022) Altay E.V.
    Real-world optimization issues have been shown to be quite difficult to solve due to the fact that their objective functions are quite complicated and there are a significant number of constraints involved. Several metaheuristics and/or different techniques for addressing constraints have been proposed as potential solutions to these challenges. A freshly constructed method has to be benchmarked against some difficult issues that occur in the current world so that its efficacy and robustness may be verified. In the body of published work, a significant number of real-world test issues have been proposed. One of these problems is the process synthesis problem, which is one of the process synthesis and design problems. In this study, the process synthesis problem has been solved with the COOT optimizer, moth-flame optimization, and Harris hawks optimization, which have been popular and very successful recently. © 2022 IEEE.