Browsing by Author "Türkoğlu, Bahaeddin"

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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: 17
Citation - Scopus: 15
Chaotic Golden Ratio Guided Local Search for Big Data Optimization
(Elsevier - Division Reed Elsevier India Pvt Ltd, 2023) Koçer, Havva Gül; Türkoğlu, Bahaeddin; Uymaz, Sait Ali
Biological systems where order arises from disorder inspires for many metaheuristic optimization techniques. Self-organization and evolution are the common behaviour of chaos and optimization algorithms. Chaos can be defined as an ordered state of disorder that is hypersensitive to initial conditions. Therefore, chaos can help create order out of disorder. In the scope of this work, Golden Ratio Guided Local Search method was improved with inspiration by chaos and named as Chaotic Golden Ratio Guided Local Search (CGRGLS). Chaos is used as a random number generator in the proposed method. The coefficient in the equation for determining adaptive step size was derived from the Singer Chaotic Map. Performance evaluation of the proposed method was done by using CGRGLS in the local search part of MLSHADE-SPA algorithm. The experimental studies carried out with the electroencephalographic signal decomposition based optimization problems, named as Big Data optimization problem (Big-Opt), introduced at the Congress on Evolutionary Computing Big Data Competition (CEC'2015). Experimental results have shown that the local search method developed using chaotic maps has an effect that increases the performance of the algorithm.& COPY; 2023 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Citation - WoS: 25
Citation - Scopus: 31
Clustering Analysis Through Artificial Algae Algorithm
(Springer Heidelberg, 2022) Türkoğlu, Bahaeddin; Uymaz, Sait Ali; Kaya, Ersin
Clustering analysis is widely used in many areas such as document grouping, image recognition, web search, business intelligence, bio information, and medicine. Many algorithms with different clustering approaches have been proposed in the literature. As they are easy and straightforward, partitioning methods such as K-means and K-medoids are the most commonly used algorithms. These are greedy methods that gradually improve clustering quality, highly dependent on initial parameters, and stuck a local optima. For this reason, in recent years, heuristic optimization methods have also been used in clustering. These heuristic methods can provide successful results because they have some mechanism to escape local optimums. In this study, for the first time, Artificial Algae Algorithm was used for clustering and compared with ten well-known bio-inspired metaheuristic clustering approaches. The proposed AAA clustering efficiency is evaluated using statistical analysis, convergence rate analysis, Wilcoxon's test, and different cluster evaluating measures ranking on 25 well-known public datasets with different difficulty levels (features and instances). The results demonstrate that the AAA clustering method provides more accurate solutions with a high convergence rate than other existing heuristic clustering techniques.
Citation - WoS: 6
Citation - Scopus: 6
A Novel Diversity Guided Galactic Swarm Optimization With Feedback Mechanism
(Ieee-Inst Electrical Electronics Engineers Inc, 2024) Uymaz, Oğuzhan; Türkoğlu, Bahaeddin; Kaya, Ersin; Asuroglu, Tunc
Galactic Swarm Optimization (GSO) is an optimization method inspired by the movements of stars and star clusters in the galaxy. This method aims to find the best solution in two phases using other known optimization methods. The first phase explores the search space, while the second phase tries to refine the best solution. In GSO, the population of the best individuals obtained from the first phase is used as the initial population for the second phase. This process is repeated until the stopping criterion is met. Although the knowledge obtained from the first phase is transferred to the second phase in GSO, there is no knowledge transfer from the second phase to the first phase. In this study, we propose a modification where the knowledge obtained in the second phase is transferred back to the first phase. Additionally, the Particle Swarm Optimization (PSO) method, used as the search method in the original study, has a fast convergence problem, which hinders an effective discovery process in the first phase of GSO. To address this, the proposed diversity-guided modification regulates population diversity and enhances exploration. To demonstrate the performance of the proposed method, twenty-six traditional benchmark functions and three engineering design problems were used. The proposed method was compared with the original GSO and six current optimization methods. The results of the experimental study were analyzed using statistical tests. The experimental results and analyses show that the proposed method performs successfully.
Citation - WoS: 52
Citation - Scopus: 61
Training Multi-Layer Perceptron With Artificial Algae Algorithm
(ELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD, 2020) Türkoğlu, Bahaeddin; Kaya, Ersin
Artificial Neural Networks are commonly used to solve problems in many areas, such as classification, pattern recognition, and image processing. The most challenging and critical phase of an Artificial Neural Networks is related with its training process. The main challenge in the training process is finding optimal network parameters (i.e. weight and biase). For this purpose, numerous heuristic algorithms have been used. One of them is Artificial Algae Algorithm, which has a nature-inspired metaheuristic optimization algorithm. This algorithm is capable of successfully solving a wide variety of numerical optimization problems. In this study, Artificial Algae Algorithm is proposed for training Artificial Neural Network. Ten classification datasets with different degrees of difficulty from the UCI database repository were used to compare the proposed method performance with six well known swarm-based optimization and backpropagation algorithms. The results of the study show that Artificial Algae Algorithm is a reliable approach for training Artificial Neural Networks. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.
Veri Bilimi ve Mühendislik Optimizasyon Problemlerinin Çözümü için Yeni Bir Yaklaşım: Kaotik Yapay Alg Algoritması
(Konya Teknik Üniversitesi, 2022) Türkoğlu, Bahaeddin; Kaya, Ersin; Uymaz, Sait Ali
Optimizasyon, bir problemin çözüm uzayındaki en uygun çözümü bulma, verilen kısıtlar altında eniyileme işlemidir. Günümüz dünyasında minimum maliyet ile maksimum verimliliğinin hedeflendiği birçok alanda yaygın olarak kullanılmaktadır. Son yıllarda, gerçek dünya problemlerinin giderek artan karmaşıklığı ve zorluğu, daha güvenilir optimizasyon tekniklerine, özellikle metasezgisel optimizasyon algoritmalarına daha fazla ihtiyaç duyulmasına neden olmuştur. Yapay Alg Algoritması (AAA), mikro alglerin karakteristiklerinden ve yaşam davranışlarından esinlenerek geliştirilmiş metasezgisel bir optimizasyon algoritmasıdır. Çeşitli alanlardaki birçok gerçek dünya problemini başarıyla çözerek popüler metasezgisel algoritmalardan birisi haline gelmiştir. Bununla birlikte diğer metasezgisel optimizasyon algoritmalarına benzer şekilde AAA da erken yakınsama ve yerel minimumlara sıkışma eğilimi göstermektedir. Bu problemleri aşmak için algoritmanın yapısının güçlendirilmesi gerekmektedir. Metasezgisel optimizasyon algoritmalarının karakteristiklerini belirleyen iki önemli arama stratejisi vardır. Bunlardan birisi keşif/çeşitlendirme diğeri sömürü/yoğunlaştırmadır. Keşif süreci, arama uzayını küresel olarak keşfetme yeteneğidir. Bu yetenek, yerel optimumdan kaçınma ve yerel optimuma takılınca kurtulabilme kabiliyetidir. Sömürü süreci ise çözümün uygunluğunu yerel olarak iyileştirmek için, mevcut çözümün yakınındaki muhtemel çözümleri keşfetme yeteneğidir. Bir metasezgisel algoritmanın performansının mükemmel olması bu iki strateji arasındaki dengeye bağlıdır. Literatürde keşif ve sömürü süreçlerini güçlendirmek ve aralarındaki dengeyi oluşturmak için levy uçuşu, kuantum davranışı, yerel arama, çoklu ve zeki arama, kaos teorisi gibi çok çeşitli stratejiler geliştirilmiştir. Bu stratejilerden birisi kaos teorisinden ilham alınarak geliştirilen kaotik haritalardır. Kaotik haritalar keşif ve sömürü arasındaki dengeyi güçlendiren çok önemli performans artırma stratejisidir nitekim bu haritalar kullanılarak literatürdeki birçok metasezgisel optimizasyon algoritmasının performansı artırılmıştır. Bu tez çalışmasında AAA kaotik haritalar ile donatılarak Kaotik Yapay Alg Algoritması isminde yeni bir yaklaşım geliştirilmiş ve dört farklı problem uzayına çözüm getirmiştir. Geliştirilen bu yaklaşımla ilk olarak farklı zorluktaki otuz benchmark test fonksiyonu çözülmüştür. Daha sonra basınçlı tank tasarımı, kaynaklı kiriş tasarımı, germe sıkıştırma yayı tasarımı ve Avrupa Uzay Ajansı'ndan alınan sekiz yörünge tasarım problemi üzerinde test edilerek performansı doğrulanmıştır. Üçüncü olarak Kaotik AAA yaklaşımı, makine öğrenmesinin üç temel alanından birisi olan gözetimsiz öğrenmedeki paylaştırmalı kümeleme problemine uygulanarak performansı analiz edilmiştir. Dördüncü olarak da önerilen kaotik AAA yaklaşımının, makine öğrenmesi algoritmaları için kaçınılmaz kritik bir önişleme süreci olan öznitelik seçiminde kullanılmak üzere ikili versiyonu geliştirilmiştir. Bu tezde geliştirilen kaotik tabanlı yeni yaklaşım, tüm problem uzaylarında literatürdeki farklı zorluk seviyesine sahip problem setleri üzerinde çeşitli popüler algoritmalarla kıyaslanmış, Wilcoxon işaretli sıralar testi ve Friedman istatistiksel testleri yapılarak güvenilirliği sağlanmış ve kıyaslanan algoritmalardan daha performanslı olduğu doğrulanmıştır.