Profile URL: https://hdl.handle.net/20.500.13091/11686
Job Title:Prof. Dr.
Email Address:mgunduz@ktun.edu.tr
Main Affiliation:10.01. Department of Computer Engineering
Status: Current Staff
ORCID:
0000-0003-4864-1926
0000-0003-4864-1926Scopus ID:
36168144300
36168144300YÖK Akademik: 9260030041B4527D
Google Scholar:
w60yFukAAAAJ
w60yFukAAAAJWeb of Science ID:
AAD-3921-2022
AAD-3921-2022Name Variants:
Gündüz, M. Gunduz, Mesut
23 results
Scholarly Output Search Results
Now showing 1 - 10 of 23
Article Moaaa/D: a Decomposition-Based Novel Algorithm and a Structural Design Application(Springer Science and Business Media Deutschland GmbH, 2024-06-14) Altiok, M.; Gündüz, M.When real-world engineering challenges are examined adequately, it becomes clear that multi-objective need to be optimized. Many engineering problems have been handled utilizing the decomposition-based optimization approach according to the literature. The performance of multi-objective evolutionary algorithms is highly dependent on the balance of convergence and diversity. Diversity and convergence are not appropriately balanced in the decomposition technique, as they are in many approaches, for real-world problems. A novel Multi-Objective Artificial Algae Algorithm based on Decomposition (MOAAA/D) is proposed in the paper to solve multi-objective structural problems. MOAAA/D is the first multi-objective algorithm that uses the decomposition-based method with the artificial algae algorithm. MOAAA/D, which successfully draws a graph on 24 benchmark functions within the area of two common metrics, also produced promising results in the structural design problem to which it was applied. To facilitate the design of the "rectangular reinforced concrete column" using MOAAA/D, a solution space was derived by optimizing the rebar ratio and the concrete quantity to be employed. © The Author(s) 2024.Doctoral Thesis Meyve Sineği Optimizasyon Algoritmasının Performansını İyileştirmek için Yeni Yaklaşımlar(Konya Teknik Üniversitesi, 2019) İşcan, Hazım; Gündüz, MesutOptimizasyon problemlerinin çözümü son yıllarda dikkat çeken bir konu haline gelmiştir. Bu problemlerin çözümü için birçok meta-sezgisel yöntem geliştirilmiştir. Meta-sezgisel yöntemler optimum çözümü garanti etmezler. Meta-sezgisel yöntemler ile makul zamanda kabul edilebilir çözümler bulmak amaçlanır. Meta-sezgisel yöntemler çoğunlukla probleme özel olmazlar. Meta-sezgisel yaklaşımlar genel amaçlıdır, esnektir ve problemlere uyarlanabilirler. Meta-sezgisel yöntemler bu özelliklerinden dolayı optimizasyon problemlerin çözümünde son yıllarda yoğun olarak kullanılmaktadır. Meyve Sineği Optimizasyon Algoritması (FOA) 2011 yılında sunulmuş bir meta-sezgisel algoritmadır. Meyve sineğinin yiyecek arama davranışından esinlenerek önerilmiştir. FOA basit yapılı, dizayn parametresi az, optimizasyon problemlerine kolay uyarlanabilir, anlaşılması ve programlanması kolay bir meta-sezgisel yaklaşımdır. Bu tür avantajları olmasına rağmen dezavantajları da mevcuttur. Lokal optimuma çabuk takılır. Karar fonksiyonu her zaman pozitiftir. Güncelleme stratejisi [-1, 1] aralığında olduğu için küçüktür. Bu çalışmada, FOA'nın dezavantajlarını gidermek, algoritmanın performansını iyileştirmek ve daha kaliteli sonuçlar üretmesini sağlamak hedeflenmiştir. Bu amaçla FOA'da üç farklı geliştirme yapılmıştır. İlk geliştirmede FOA'ya işaret parametreleri ilave edilmiş ve SFOA olarak adlandırılmıştır. İkincide, FOA'nın karar verme stratejisi iki aşamalı hale getirilmiş ve saFOA olarak adlandırılmıştır. Üçüncüde, FOA'nın arama esnasında en kötü çözümlerinde dikkate alındığı iki farklı versiyon geliştirilmiş ve pFOA_v1 ve pFOA_v2 olarak adlandırılmıştır. Yeni önerilen FOA sürümlerinin performansı iyi bilinen 21 sayısal kıyaslama fonksiyonunda test edilerek araştırılmıştır. Elde edilen deneysel sonuçlar, literatürde iyi bilinen meta-sezgisel algoritmalar ile kıyaslanmıştır. Deneysel sonuçlar, önerilen FOA sürümlerinin sürekli optimizasyon problemleri için karşılaştırılabilir, başarılı ve rekabetçi sonuçlar ürettiğini göstermektedir.Article Citation - WoS: 48Citation - Scopus: 53Petrogram: an Excel-Based Petrology Program for Modeling of Magmatic Processes(CHINA UNIV GEOSCIENCES, BEIJING, 2021-01-01) Mesut Gündüz; Asan, Kürşad; Gunduz, MesutPetroGram is an Excel(C) based magmatic petrology program that generates numerical and graphical models. PetroGram can model the magmatic processes such as melting, crystallization, assimilation and magma mixing based on the trace element and isotopic data. The program can produce both inverse and forward geochemical models for melting processes (e.g. forward model for batch, fractional and dynamic melting, and inverse model for batch and dynamic melting). However, the program uses a forward modeling approach for magma differentiation processes such as crystallization (EC: Equilibruim Crystallization, FC: Fractional Crystallization, IFC: Imperfect Fractional Crystallization and In-situ Crystallization), assimilation (AFC: Assimilation Fractional Crystallization, Decoupled FC-A: Decoupled Fractional Crystallization and Assimillation, A-IFC: Assimilation and Imperfect Fractional Crystallization) and magma mixing. One of the most important advantages of the program is that the melt composition obtained from any partial melting model can be used as a starting composition of the crystallization, assimilation and magma mixing. In addition, PetroGram is able to carry out the classification, tectonic setting, multi-element (spider) and isotope correlation diagrams, and basic calculations including Mg#, Eu/Eu*, epsilon(S)(r) and epsilon(Nd) widely used in magmatic petrology.Other Geochemistry and Petrology of the Unimodal Versus Bimodal Neogene Volcanism in the Konya Volcanic Field, Central Anatolia, Turkey(2023) Korkmaz, Gülin Gençoğlu; Kurt, Hüseyin; Gündüz, Mesut; Asan, KürşadArticle Citation - WoS: 67Citation - Scopus: 79Djaya: a Discrete Jaya Algorithm for Solving Traveling Salesman Problem(ELSEVIER, 2021-07-01) Gündüz, Mesut; Aslan, MuratJaya algorithm is a newly proposed stochastic population-based metaheuristic optimization algorithm to solve constrained and unconstrained continuous optimization problems. The main difference of this algorithm from the similar approaches, it uses best and worst solution in the population in order improve the intensification and diversification of the population, and this provides discovering potential solutions on the search space of the optimization problem. In this study, we propose discrete versions of the Jaya by using two major modifications in the algorithm. First is to generate initial solutions by using random permutations and nearest neighborhood approach to create population. Second is the update rule of the basic Jaya algorithm rearranged to solve discrete optimization problems. Due to characteristics of the discrete optimization problem, eight transformation operators are used for the discrete variants of the proposed algorithm. Based on these modifications, the discrete Jaya algorithm, called DJAYA, has been applied to solve fourteen different symmetric traveling salesman problem, which is one of the famous discrete problems in the discrete optimization. In order to improve the obtained best solution from DJAYA, 2-opt heuristic is also applied to the best solution of DJAYA. Once population size, search tendency and the other parameters of the proposed algorithm have been analyzed, it has been compared with the state-of-art algorithms and their variants, such as Simulated Annealing (SA), Tree-Seed Algorithm (TSA), State Transition Algorithm (STA) Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Genetic Algorithm (GA) and Black Hole (BH). The experimental results and comparisons show that the proposed DJAYA is highly competitive and robust optimizer for the problem dealt with the study. (C) 2021 Elsevier B.V. All rights reserved.Doctoral Thesis Ayrık Optimizasyon Problemlerinin Çözümü için Jaya Algoritması Tabanlı Yeni Yaklaşımlar(Konya Teknik Üniversitesi, 2020) Aslan, Murat; Gündüz, MesutJaya algoritması, kısıtlı ve kısıtsız sürekli optimizasyon problemlerinin çözümü için Rao (2016) tarafından literatüre kazandırılan popülasyon tabanlı, stokastik bir metasezgisel algoritmadır. Bu tez kapsamında ikili ve ayrık tam sayı optimizasyon problemlerinin çözümü için Jaya algoritması tabanlı yeni yaklaşımlar geliştirilmiştir. Temel Jaya algoritması sürekli optimizasyon problemlerinin çözümü için geliştirildiğinden dolayı, karar değişilenleri '0' ya da '1' değerlerini alabilen ikili optimizasyon problemlerinin üzerine doğrudan uygulanamaz. Bu kapsamda temel Jaya algoritmasının konum güncelleme mekanizmasında bazı değişiklikler yapılmış olup, ikili optimizasyon problemlerinin çözümü için Jaya algoritması tabanlı yeni yaklaşımlar geliştirilmiştir. Geliştirilen ilk yaklaşım JayaX olarak adlandırılan; temelini Jaya algoritması ve 'özel veya' (XOR) lojik fonksiyonundan alan yaklaşımdır. Diğer yaklaşım ise JayaX algoritmasının yerel arama yönünü iyileştirmek için JayaX-LSM olarak adlandırılan, önerilen JayaX algoritmasının ve LSM olarak adlandırılan yerel arama modülünün birlikte kullanılması ile geliştirilen yaklaşımdır. İkili optimizasyon problemlerinin çözümü için önerilen algoritmaların performans analizi için deney aşamasında: Kapasitesiz tesis yerleştirme problemi (KTYP), CEC 2015 nümerik fonksiyonları ve rüzgâr türbini yerleştirme problemi kullanılmıştır. İlk deneysel analiz KTYP problemi için yapılmıştır. Önerilen algoritmaların performansını analiz etmek ve doğrulamak için deneylerde 15 farklı KTYP kullanılmıştır. Önerilen algoritmalar yakın zamanda literatüre kazandırılmış PSO, ABC, TSA, DE ve GA tabanlı başarılı ikili optimizasyon algoritmalarının sonuçları ile karşılaştırılmıştır. Elde edilen deneysel sonuçlara göre, önerilen algoritmalar, KTYP'yi çözme konusunda, karşılaştırılan diğer algoritmalar ile eşit ya da daha iyi sonuçlar elde etmiştir. İkinci deneysel analiz ise 15 kıyas probleminden oluşan CEC 2015 nümerik veri seti üzerine olmuştur. Bu analizde, JayaX-LSM algoritması SabDE, BQIGSA, GBABC, BHTPSO-QI, BLDE ve SBHS algoritmalarının sonuçları ile karşılaştırılmıştır ve elde edilen deneysel sonuçlar dikkate alındığında JayaX-LSM algoritması, karşılaştırılan algoritmalar ile rekabetçi ya da daha iyi çözümler elde etmiştir. Bu bölümde yapılan son deneysel analiz ise rüzgâr türbini yerleştirme problemi için yapılmıştır. Deneylerde 10×10 ve 20×20'lik olmak üzere iki farklı ızgara yapısı kullanılmıştır. Deneysel sonuçlara göre, JayaX-LSM algoritması karşılaştırmalarda kullanılan GA tabanlı ikili yöntemler, BIWO, BPSO-TVAC, EA, NGHS, DGHS ve binAAA algoritmalarına benzer ya da daha iyi çözümler üretmiştir. Geliştirilen bir diğer yöntem ise ayrık tam sayı optimizasyon problemlerinin çözümü için Jaya algoritması tabanlı DJaya olarak adlandırılan Ayrık Jaya algoritmasıdır. Temel Jaya algoritmasının ayrıklaştırma işlemi için güncelleme mekanizmasında takas, öteleme ve simetri olarak adlandırılan komşuluk operatörleri kullanılmıştır. DJaya'da başlangıç popülasyonu oluşturulurken (N-1) tane aday çözüm rastgele permütasyon ile oluşturulurken, ilk aday çözüm en yakın komşu turu sezgiseli ile oluşturulmaktadır. Ayrıca DJaya'nın elde ettiği çözümlerin kalitesinin arttırılması amacıyla 2-opt yerel arama algoritması da kullanılmıştır. DJaya algoritmasının başarısını ve etkinliğini göstermek amacıyla, deneylerde 14 farklı gezgin satıcı problemi (GSP) kullanılmıştır. Elde edilen deneysel sonuçlar yakın zamanda literatüre kazandırılan başarılı algoritmaların sonuçları ile karşılaştırılmıştır. Deneysel bulgulara göre, DJaya algoritması gezgin satıcı probleminin çözümü için karşılaştırılan algoritmalardan daha başarılı ya da rekabetçi çözümler elde etmiştir.Article Citation - WoS: 9Citation - Scopus: 10A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019) İşcan, Hazım; Kıran, Mustafa Servet; Gündüz, MesutFruit fly optimization algorithm (FOA) is one of the swarm intelligence algorithms proposed for solving continuous optimization problems. In the basic FOA, the best solution is always taken into consideration by the other artificial fruit flies when solving the problem. This behavior of FOA causes getting trap into local minima because the whole population become very similar to each other and the best solution in the population during the search. Moreover, the basic FOA searches the positive side of solution space of the optimization problem. In order to overcome these issues, this study presents two novel versions of FOA, pFOA_v1 and pFOA_v2 for short, that take into account not only the best solutions but also the worst solutions during the search. Therefore, the proposed approaches aim to improve the FOA's performance in solving continuous optimizations by removing these disadvantages. In order to investigate the performance of the novel proposed FOA versions, 21 well-known numeric benchmark functions are considered in the experiments. The obtained experimental results of pFOA versions have been compared with the basic FOA, SFOA which is an improved version of basic FOA, SPSO2011 which is one of the latest versions of particle swarm optimization, firefly algorithm called FA, tree seed algorithm TSA for short, cuckoo search algorithm briefly CS, and a new optimization algorithm JAYA. The experimental results and comparisons show that the proposed versions of FOA are better than the basic FOA and SFOA, and produce comparable and competitive results for the continuous optimization problems.Article Citation - WoS: 3Citation - Scopus: 1The Role of Magma Recharge and Mixing in Producing Compositional Modality in Post-Collisional Volcanic Rocks, Konya Volcanic Field, Central Anatolia (türkiye)(Elsevier Ltd, 2024-12-01) Asan, K.; Gündüz, M.; Korkmaz, G.G.; Kurt, H.The Neogene Erenlerdağ-Alacadağ (ErAVC) and Sulutas (SVC) volcanic complexes in the Konya Volcanic Field, Türkiye have distinctly different unimodal and bimodal compositional variations, respectively. They occurred in graben-like extensional basins behind the retreating Cyprus subduction zone between the African and Eurasian plates. We here investigate their compositional modality by using new and published whole-rock major and trace element and Sr-Nd-Pb isotope data. Both complexes are characterized by basaltic to rhyodacitic high-K calc-alkaline rocks with the geochemical signatures of orogenic volcanism, except for minor alkaline rocks in the SVC. Mass-balance models suggest that major element variations can be largely explained by the fractional crystallization of amphibole, plagioclase, and Fe-Ti oxides. However, Sr-Nd-Pb isotopes show correlations with SiO2 indicating that open-system processes played a role in their differentiation. Modeling of AFC (Assimilation and Fractional Crystallization) involving a recharge situation shows that low degrees of crustal assimilation (rate of assimilation/rate of fractional crystallization, r < 0.2 and crust/magma ratio, ρ: 15–16 %) of lower and upper crust-like rocks was involved in the differentiation of the ErAVC and SVC, respectively. However, the modeling suggests that magma recharge (β: rate of magma recharge/rate of assimilation) was more efficient in the ErAVC (β: 3.45, % ∼52.5 rate of recharge) relative to that of the SVC (β: 2.15, % ∼36.55 rate of recharge). We conclude that for the ErAVC and SVC, different parental magmas derived from the subduction-modified mantle source followed distinct differentiation paths in the crust, and their compositional modality was mainly controlled by the magma recharge and mixing process. © 2024 Elsevier LtdArticle Citation - WoS: 4Citation - Scopus: 4Geostats: an Excel-Based Data Analysis Program Applying Basic Principles of Statistics for Geological Studies(Springer Science and Business Media Deutschland GmbH, 2021-10-02) Mesut Gündüz; Asan, K.; Gunduz, MesutStatistical evaluation of the data collected from field and laboratory is an important task in the earth sciences. The aim of a geological study is to reveal a scientific result from the earth, but the conclusions must be based on the analytical inferences, as in natural and engineering sciences. Therefore, the importance of data analysis increases depending on the improvement of technological methods in geology. The purpose of data analysis in geology is to examine the rate at which a feature changes within the population. The geological data may be lithological, textural (e.g. grain size and shape), structural (e.g. bedding, fault, foliation, jointing or lineation etc.) and chemical (e.g. major and trace elements, isotope ratios etc.) measurements of the rock, mineral, fossil, soil or water specimens. GEOstats is an excel-based data analysis program that provides graphical and numerical results, and data simulation/statistical modeling (e.g. simple regression analysis, box plot, Q–Q plot, XYZ plot, sample distribution and classification) of samples representing a population for geologists and other researchers as well. The program can perform both simple data analysis (e.g. basic statistical calculations) of numerical results and highly complex multivariate analysis such as cluster analysis, principal component analysis and common factor analysis. GEOstats is also able to carry out the error bars analysis by using relative standard deviation values for different confidence intervals. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Article Citation - WoS: 10Citation - Scopus: 7A Jaya-Based Approach To Wind Turbine Placement Problem(TAYLOR & FRANCIS INC, 2020-09-21) Aslan, Murat; Gündüz, Mesut; Kıran, Mustafa ServetRenewable energy resources are natural, clean, economical, and never-ending energy resources. Wind energy is an important clean, cheap, and easy applicable energy sources. On account of this, generation of the energy from wind technology has been raised day by day because of the competition with fossil-fuel power production methods. By depending on increases the number of turbines located in the wind farm, the average power obtains from each wind turbine appreciable reduces due to the existence of wake effects within the wind farm. Therefore, the optimal placement of turbines in a wind farm provides to get optimum wind energy from the wind farm. When the place where the wind turbines are located is considered as NxN grid, a wind turbine can be established to each cell of this grid. Whether a wind turbine is replaced to each cell of the grid or not can be modeled as a binary-based optimization problem. In this study, a Jaya-based binary optimization algorithm is proposed to determine which cells are used for wind turbine replacement. In order to justify the efficiency of the proposed approach, two different test cases are considered, and the solutions produced by the proposed approach are compared with the solutions of the swarm intelligence or evolutionary computation methods. According to the experiments and comparisons the Jaya-based binary approach shows a superior performance than compared approaches in terms of cost and power effectiveness. While the efficiency of the Jaya-based approach is 92.2% with 30 turbines replacement on 10 x 10 grid, the efficiency of the Jaya-based binary method is 95.7% with 43 turbines replacement on 20 x 20 grid.
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Research Topics
Domains
Physical Sciences
Fields
Computer ScienceEngineering
Subfields
Artificial IntelligenceIndustrial and Manufacturing EngineeringComputational Theory and MathematicsComputer Networks and Communications
Specific Research Areas
Metaheuristic Optimization Algorithms Research
Vehicle Routing Optimization Methods
Advanced Multi-Objective Optimization Algorithms
Evolutionary Algorithms and Applications
Energy Efficient Wireless Sensor Networks
Sustainable Development Goals
7AFFORDABLE AND CLEAN ENERGY
2
Research Products
14LIFE BELOW WATER
1
Research Products
Documents
23
Citations
1372
h-index
15
Documents
9
Citations
122
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Selçuk University | 18 |
| Konya Technical University | 8 |
| Şırnak University | 3 |
| Tokat Gaziosmanpaşa Üniversitesi | 2 |
| University of Eastern Finland | 1 |
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Data obtained from OpenAlex
| Journal | Count |
|---|---|
| APPLIED SOFT COMPUTING | 2 |
| Applied Soft Computing | 1 |
| Comptes Rendus - Geoscience | 1 |
| Earth Science Informatics | 1 |
| ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS | 1 |
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Scholarly Output
23
Articles
17
Views / Downloads
96/378
Supervised MSc Theses
1
Supervised PhD Theses
3
WoS Citation Count
276
Scopus Citation Count
297
Patents
0
Projects
0
WoS Citations per Publication
12.00
Scopus Citations per Publication
12.91
Open Access Source
13
Supervised Theses
4
Scopus Quartile Distribution
Competency Cloud
