Bilgisayar ve Bilişim Fakültesi Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/10834

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Citation - WoS: 44
Citation - Scopus: 47
Binary Artificial Algae Algorithm for Feature Selection
(Elsevier, 2022) Türkoğlu, Bahaeddin; Uymaz, Sait Ali; Kaya, Ersin
In this study, binary versions of the Artificial Algae Algorithm (AAA) are presented and employed to determine the ideal attribute subset for classification processes. AAA is a recently proposed algorithm inspired by microalgae's living behavior, which has not been consistently implemented to determine ideal attribute subset (feature selection) processes yet. AAA can effectively look into the feature space for ideal attributes combination minimizing a designed objective function. The proposed binary versions of AAA are employed to determine the ideal attribute combination that maximizes classification success while minimizing the count of attributes. The original AAA is utilized in these versions while its continuous spaces are restricted in a threshold using an appropriate threshold function after flattening them. In order to demonstrate the performance of the presented binary artificial algae algorithm model, an experimental study was conducted with the latest seven highperformance optimization algorithms. Several evaluation metrics are used to accurately evaluate and analyze the performance of these algorithms over twenty-five datasets with different difficulty levels from the UCI Machine Learning Repository. The experimental results and statistical tests verify the performance of the presented algorithms in increasing the classification accuracy compared to other state-of-the-art binary algorithms, which confirms the capability of the AAA algorithm in exploring the attribute space and deciding the most valuable features for classification problems. (C) 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 21
Citation - Scopus: 24
Boosting the Oversampling Methods Based on Differential Evolution Strategies for Imbalanced Learning
(Elsevier, 2021) Korkmaz, Sedat; Sahman, Mehmet Akif; Çınar, Ahmet Cevahir; Kaya, Ersin
The class imbalance problem is a challenging problem in the data mining area. To overcome the low classification performance related to imbalanced datasets, sampling strategies are used for balancing the datasets. Oversampling is a technique that increases the minority class samples in various proportions. In this work, these 16 different DE strategies are used for oversampling the imbalanced datasets for better classification. The main aim of this work is to determine the best strategy in terms of Area Under the receiver operating characteristic (ROC) Curve (AUC) and Geometric Mean (G-Mean) metrics. 44 imbalanced datasets are used in experiments. Support Vector Machines (SVM), k-Nearest Neighbor (kNN), and Decision Tree (DT) are used as a classifier in the experiments. The best results are produced by 6th Debohid Strategy (DSt6), 1th Debohid Strategy (DSt1), and 3th Debohid Strategy (DSt3) by using kNN, DT, and SVM classifiers, respectively. The obtained results outperform the 9 state-of-the-art oversampling methods in terms of AUC and G-Mean metrics (C) 2021 Elsevier B.V. All rights reserved.
Citation - Scopus: 15
Chaos Theory in Metaheuristics
(Elsevier, 2023) Türkoğlu, B.; Uymaz, S.A.; Kaya, E.
Metaheuristic optimization is the technique of finding the most suitable solution among the possible solutions for a particular problem. We encounter many problems in the real world, such as timetabling, path planning, packing, traveling salesman, trajectory optimization, and engineering design problems. The two main problems faced by all metaheuristic algorithms are being stuck in local optima and early convergence. To overcome these problems and achieve better performance, chaos theory is included in the metaheuristic optimization. The chaotic maps are employed to balance the exploration and exploitation efficiently and improve the performance of algorithms in terms of both local optima avoidance and convergence speed. The literature shows that chaotic maps can significantly boost the performance of metaheuristic optimization algorithms. In this chapter, chaos theory and chaotic maps are briefly explained. The use of chaotic maps in metaheuristic is presented, and an enhanced version of GSA with chaotic maps is shown as an application. © 2023 Elsevier Inc. All rights reserved.
Citation - WoS: 20
Citation - Scopus: 23
A Tree Seed Algorithm With Multi-Strategy for Parameter Estimation of Solar Photovoltaic Models
(Elsevier, 2024) Beskirli, Ayse; Dag, Idiris; Kiran, Mustafa Servet
Tree seed algorithm, which is one of the metaheuristics algorithms recently proposed for the solution of continuous optimization problems, has an effective algorithmic structure inspired by the relation between trees and seeds. At the same time, the use of two different solution generation mechanisms by depending on the control parameter in TSA aims to balance the exploration and exploitation capabilities of the algorithm. However, when the structure of the algorithm is examined in detail, it is seen that there are some disadvantages such as loss of population diversity and getting stuck in local minimums. To overcome these disadvantages in the basic algorithm, three different approaches (self-adaptive weighting mechanism, chaotic elite learning approach and experience-based learning method) were proposed to TSA under the name of multi-strategies in this study. The algorithm improved with these approaches is named as the multi-strategy-based tree seed algorithm (MS-TSA). MS-TSA was first tested on CEC2017 functions. Then MS-TSA was applied to the problems in the CEC2020 competition and compared with the results of the best performing algorithms in this competition. As a result of the comparisons, MS-TSA was found to be a competitive method on solving benchmark functions. Then, parameter estimation of single diode, double diode and photovoltaic module models using the input data of various solar panels was carried out by the MS-TSA. The results obtained with MS-TSA were compared with both the results of the basic TSA and the results of well-known algorithms in the literature. The results obtained are 9.8642E-04, 9.8356E-04, 2.4251E-03, 1.7534E-03 respectively. As a result of the comparative analysis, the lowest RMSE value was obtained by MS-TSA. In addition, comprehensive performance analyzes of the algorithms were made with the convergence curve, boxplots, current (I)- voltage (V) and power (P)- voltage (V) charac- teristic curves obtained according to the experimental results. As a result of the experiments and analyses, MS- TSA was found to be a more successful method than the compared algorithms in parameter estimation of PV models.

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