Browsing by Author "Karakoyun, M."

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Citation - Scopus: 6
An Effective Binary Dynamic Grey Wolf Optimization Algorithm for the 0-1 Knapsack Problem
(Springer, 2025) Erdoğan, F.; Karakoyun, M.; Gülcü, Ş.
Metaheuristic algorithms are recommended and frequently used methods for solving optimization problems. Today, it has been adapted to many challenging problems and its successes have been identified. The grey wolf optimizer (GWO) is one of the most advanced metaheuristics. Because of the advantages it provides, GWO has been applied to solve many different problems. In this study, a new variant of GWO, the Binary Dynamic Grey Wolf Optimizer (BDGWO), is proposed for the solution of binary optimization problems. The main contributions of BDGWO compared to other binary GWO variants are that it uses the XOR bitwise operation to binarize and is based on the dynamic coefficient method developed to determine the effect of the three dominant wolves (alpha, beta, and delta) in the algorithm. BDGWO is a simple, feasible, and successful method that strikes a balance between local search and global search in solving binary optimization problems. To determine the success and accuracy of the proposed BDGWO, it was tested on the 0-1 knapsack problem (0-1 KP), which is classified as an NP-Hard problem. The BDGWO was compared with 17 different binary methods across a total of 55 data sets from three different studies published in the last four years. The Friedman test was applied to interpret the experimental results more easily and to evaluate the algorithm results statistically. As a result of the experiments, it has been proven that the BDGWO is an effective and successful method in accordance with its purpose. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Citation - Scopus: 2
A Novel Binary Grey Wolf Optimizer Algorithm With a New Dynamic Position Update Mechanism for Feature Selection Problem: A Novel Binary Grey Wolf Optimizer Algorithm With a New Dynamic Position…: F. Erdoğan Et Al.
(Springer Science and Business Media Deutschland GmbH, 2024) Erdoğan, F.; Karakoyun, M.; Gülcü, Ş.
Feature selection (FS) is one of the basic preprocessing steps in data mining and is a challenging binary optimization problem. FS is the process of determining the subset that can best represent the dataset by removing features that have little impact from a given dataset without affecting performance and accuracy. In this paper, Binary Dynamic Grey Wolf Optimization Algorithm (binDGWO) is proposed for the solution of binary optimization problems. To binaryize the Grey Wolf Optimization Algorithm (GWO), the original position update equation was binaryized using the logical XOR operator to achieve a balance between local search and global search. In addition, a simple and effective innovation has been introduced to the position update equation with the dynamic coefficient method. This method has been developed to make the affected solution better by determining and applying the effects of the wolves in the lead team on the position according to the solution quality. The performance of binDGWO over FS is compared to the performance of over 20 different algorithms, including binary variants of GWO and different binary metaheuristics. 41 datasets with different numbers of samples and features were used in the experiments. Various performance metrics were used to determine the superiority of the methods over each other. In addition, the Friedman test was performed to statistically evaluate the results of the methods. According to the performance metrics and the Friedman test results, it was seen that the proposed algorithm has better results than other binary variants of GWO, and when the comparison results with other metaheuristic algorithms are examined, it is generally more successful and effective. In conclusion, it can be said that binDGWO is a simple, effective, and efficient binary method and it achieves its purpose. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.