Profile URL: https://hdl.handle.net/20.500.13091/11736
Job Title:Doç. Dr.
Email Address:ismailkoc@ktun.edu.tr
Main Affiliation:10.02. Department of Software Engineering
Status: Current Staff
ORCID:
0000-0003-1311-5918
0000-0003-1311-5918Scopus ID:
57190306475
57190306475YÖK Akademik: B0932F36647B52AF
Google Scholar:
ap7J0I4AAAAJ
ap7J0I4AAAAJWeb of Science ID:
ABF-9636-2021
ABF-9636-2021Name Variants:
Koc, Ismail
20 results
Scholarly Output Search Results
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Conference Object Energy Demand Projection of Turkey Based on Coot Bird Metaheuristic Optimizer(2021) Koç, İsmailThe energy demand projection forms the basis of realistic energy planning. In this study, from 1979 to 2011, a 33- year data set including gross domestic product (GDP), population, import and export was used for energy demand forecasting in Turkey. Using this data set, two different energy estimation models, linear (COOT_L) and quadratic (COOT_Q), were developed with the Coot bird metaheuristic optimizer. These models were compared with different optimization algorithms in the literature. When the experimental results were examined, the COOT_L model produced a more successful result than the results of other algorithms. Furthermore, COOT_Q was seen to become more successful than PSO and ACO. In addition to these, the COOT_L and COOT_Q models forecasted the future energy demand between 2012 and 2030 in Turkey and their results were compared with those of DE methods. When the results were examined, while DE and COOT_L produced similar results in linear form, certain differences were observed among the results of COOT_Q and DE.Article Coronavirüs Sürü Bağışıklığı Algoritması ile Otsu Tabanlı Optimal Çok Düzeyli Görüntü Eşiği(2023-01-31) Koç, İsmailEşik seçimi, görüntü bölütlemede önemli bir rol oynamaktadır. Eşik seçimiyle ilgili en faydalı yöntemler olarak minimum hata yöntemi, iteratif yöntem, entropi yöntemi ve Otsu yöntemi bilinmektedir. Bu çalışmada eşikleme yöntemi olarak Otsu tekniği kullanılmaktadır. Eşik sayısının (K) artmasına bağlı olarak problemin karmaşıklık düzeyi üstel olarak artacağı için matematiksel yöntemler yerine sürü zekâsı algoritması kullanılması daha uygun görülmektedir. Bundan dolayı, bu çalışmada sürü zekâsı algoritması olarak da son yıllarda literatüre kazandırılmış olan Coronavirüs sürü bağışıklığı algoritması (CHIO) kullanılmaktadır. Deneysel çalışmalarda test verisi olarak altı farklı görüntü kullanılmaktadır. K değeri bu çalışmada 2, 3, 4 ve 5 olarak belirlenmektedir. Bu veri seti kullanılarak CHIO algoritması ile literatürde yer alan diferansiyel evrim (differential evolution: DE), gri kurt ( gray wolf optimizer: GWO), parçacık sürü (particle swarm optimization: PSO) algoritmaları gibi başarılı algoritmalarla eşit koşullarda kıyaslanmaktadır. Elde edilen sonuçlara göre, CHIO algoritması kullanılarak 6 test verisi üzerinde yapılan çalışmalarda K=2 olduğunda verilerin %100, K=3 ve 4 iken %83 ve son olarak K=5 iken %50’sinde en iyi sonuçları yakaladığı görülmektedir. Bu sonuçlar ışığında, CHIO algoritmasının çözüm kalitesi açısından rekabet edici olduğu tespit edilmiştir. Sonuç olarak CHIO algoritması çok düzeyli görüntü eşiği problemi için alternatif bir algoritma olabilir.Article Yeni Bir İkili Sürüş Eğitimi Tabanlı Algoritma Üzerinde Transfer Fonksiyonlarının İncelenmesi(2023-06-28) Koç, IsmaılKapasitesiz Tesis Yerleşim Problemi (UFLP), tesislerin optimal yerleşimini belirleyen NP-zor bir problemdir. UFLP, NP-Zor problem grubundan olduğu için, bu problemlerin büyük örneklerini çözmek için kesin yöntemlerin kullanılması, optimal çözümü elde etmek için gereken yüksek hesaplama süreleri nedeniyle ciddi şekilde sorun teşkil edebilir. Bu çalışmada, problemin karmaşıklığından dolayı sürü zekası algoritması tercih edilmiştir. Son yıllarda sürüş eğitimi ilkelerine dayalı olarak geliştirilen popülasyon tabanlı bir algoritma olan Sürüş eğitim tabanlı (DTBO) algoritması UFLP probleminin çözümünde kullanılmıştır. DTBO’nun temel versiyonu sürekli problemlerin çözümünü ele aldığından söz konusu algoritmanın ikili problemlerin çözümüne uyarlanması gerekmektedir. Bunun için literatürde kullanılan dokuz farklı transfer fonksiyonu yardımıyla DTBO algoritması ikili problemlerin çözümüne uygun olarak tasarlanmıştır. Deneysel çalışmalar transfer fonksiyonlarının adil kıyaslanabilmesi için eşit koşullarda altında gerçekleştirilmiştir. Gerçekleştirilen deneysel çalışmalarda dokuz transfer fonksiyonu içerisinden ikili Mode-DTBO algoritmasının en başarılı algoritma olduğu görülmektedir. Bu sonuçlara göre Mode tabanlı DTBO algoritmasının küçük, orta ve büyük ölçekli tüm problem setlerinde hem çözüm kalitesi açısından hem de zaman açısından çok başarılı olduğu görülmektedir. Ayrıca DTBO algoritması IWO (Yabani Ot Algoritması – Invasive Weed Optimization) algoritmasına ait 3 farklı transfer fonksiyonuyla (Mode, Sigmoid ve Tanh) da kıyaslanmıştır. Karşılaştırmalı sonuçlar incelendiğinde 12 problemin 8’inde (orta ve büyük ölçekli problem) Mode-DTBO yaklaşımının IWO’ya ait 3 farklı yaklaşımın hepsinden çok daha başarılı olduğu görülmüştür. Bununla beraber, küçük boyutlu 4 problem üzerinde ise Mode fonksiyonunu kullanan her iki algoritmanın da optimal değeri yakaladığı görülmüştür. Sonuç olarak, Mode-DTBO yönteminin ikili problemlerin çözümünde çok etkili bir alternatif sunacağı söylenebilir.Article Citation - WoS: 15Citation - Scopus: 17Discrete Tree Seed Algorithm for Urban Land Readjustment(Pergamon-Elsevier Science Ltd, 2022-06-01) Koç, İsmail; Atay, Yılmaz; Babaoğlu, İsmailLand readjustment and redistribution (LR) is an important approach used to realize development plans by converting rural lands to urban land and also providing urban infrastructure. The LR problem, which is a complex challenging real-world problem, is a discrete optimization problem because its structure is similar to TSP (Traveling Salesman Problem) and scheduling problems which are combinatorial optimization problems. Since classical mathematical methods are insufficient for solving NP (Nondeterministic Polynomial) optimization problems due to time limitations, meta-heuristic optimization algorithms are commonly utilized for solving these kinds of problems. In this paper, meta-heuristic algorithms including genetic, particle swarm, differential evolution, artificial bee, and tree seed algorithms are utilized for solving LR problems. The stated meta-heuristic algorithms are used by applying spatial-based crossover and mutation operators depending upon the LR problem on each algorithm. Moreover, a synthetic dataset is used to ensure that the quality of the solution obtained is acceptable to everyone, to prove an optimal solution easily. By utilizing the suggested spatial-based crossover and mutation operators, finding the ideal solution is aimed using the synthetic dataset. In addition, five different modifications on TSA (Tree-Seed Algorithm) are performed and used to solve LR problems. All the modified versions of TSA are carried out only by changing the mechanism of seed reproduction. The novel TSA approaches are respectively named as tcTSA (tournament current), tbTSA (tournament best), pbTSA (personal-best based), t2TSA (double tournament), and elTSA (elitism based). In the experimental studies, the hybrid approach, which includes the crossover and mutation operators, is successfully applied in all of the algorithms under equal conditions for a fair comparison. According to experimental results performed using the dataset, it can be clearly stated that especially t2TSA outperforms all the algorithms in terms of performance and time.Article Citation - WoS: 2B-Spline Curve Approximation by Utilizing Big Bang-Big Crunch Method(TECH SCIENCE PRESS, 2020) İnik, Özkan; Ülker, Erkan; Koç, İsmailThe location of knot points and estimation of the number of knots are undoubtedly known as one of the most difficult problems in B-Spline curve approximation. In the literature, different researchers have been seen to use more than one optimization algorithm in order to solve this problem. In this paper, Big Bang-Big Crunch method (BB-BC) which is one of the evolutionary based optimization algorithms was introduced and then the approximation of B-Spline curve knots was conducted by this method. The technique of reverse engineering was implemented for the curve knot approximation. The detection of knot locations and the number of knots were randomly selected in the curve approximation which was performed by using BB-BC method. The experimental results were carried out by utilizing seven different test functions for the curve approximation. The performance of BB-BC algorithm was examined on these functions and their results were compared with the earlier studies performed by the researchers. In comparison with the other studies, it was observed that though the number of the knot in BB-BC algorithm was high, this algorithm approximated the B-Spline curves at the rate of minor error.Article Citation - WoS: 17Citation - Scopus: 23A Fast Community Detection Algorithm Based on Coot Bird Metaheuristic Optimizer in Social Networks(Pergamon-Elsevier Science Ltd, 2022-09-01) Koç, İsmailCommunity detection (CD) is critical to understanding complex networks. Researchers have made serious efforts to develop efficient CD algorithms in this sense. Since community detection is an NP-hard problem, utilizing metaheuristic algorithms is preferred instead of classical approaches in solving the problem. For this reason, in this study, six different metaheuristic algorithms called Archimedes optimization algorithm (AOA), Atom search optimization (ASO), Coot Bird Natural Life Model (COOT), Harris Hawks Optimization (HHO), Slime Mould Algorithm (SMA) and Arithmetic Optimization Algorithm (AROA) are used in the solution of CD problems and all of which have been proposed for solving continuous problems in recent years. Since the CD problem has a discrete structure, discrete versions of all the algorithms are produced, and then the proposed discrete algorithms are adapted to the problem. In addition, in the phase of evaluating the objective function of the problem, a fast approach based on CommunityID is proposed to minimize the time cost when solving the problem, and this approach is utilized in all the algorithms when calculating the fitness value. In the experimental studies, firstly, the novel discrete algorithms are compared with each other in terms of solution quality and time and according to these results, COOT becomes the most effective and very fast algorithm. Then, the results obtained by COOT are compared with those of important studies in the literature. When compared in terms of solution quality, it is seen that the COOT algorithm is more effective than the other algorithms. In addition, it is quite obvious that all of the proposed algorithms using the CommunityID-based approach are faster than the other algorithms in the literature in terms of time. As a result, it can be said that COOT can be an effective alternative method for dealing with CD problems. In addition, the approach based on CommunityID can also be utilized in larger networks to obtain remarkable solutions in a much shorter time.Article Citation - WoS: 8Citation - Scopus: 9A Comparative Study of Swarm Intelligence and Evolutionary Algorithms on Urban Land Readjustment Problem(ELSEVIER, 2021-02-01) Koç, İsmail; Babaoğlu, İsmailLand Readjustment and redistribution (LR) is a land management tool that helps regular urban development with the contribution of landowners. The main purpose of LR is to transform irregularly developed land parcels into suitable forms. Since it is necessary to handle many criteria simultaneously to solve LR problems, classical mathematical methods can be insufficient due to time limitation. Since LR problems are similar to traveling salesman problems and typical scheduling problems in terms of structure, they are kinds of NP-hard problems in combinatorial optimization. Therefore, metaheuristic algorithms are used in order to solve NP-hard problems instead of classical methods. At first, in this study, an effective problem-specific objective function is proposed to address the main criteria of the problem. In addition, a map-based crossover operator and three different mutation operators are proposed for the LR, and then a hybrid approach is implemented by utilizing those operators together. Furthermore, since the optimal value of the problem handled in real world cannot be exactly estimated, a synthetic dataset is proposed as a benchmarking set in LR which makes the success of algorithms can be objectively evaluated. This dataset consists of 5 different problems according to number of parcel which are 20, 40, 60, 80 and 100. Each problem set consists of 4 sub-problems in terms of number of landowners per-parcel which are 1, 2, 3 and 4. Therefore, the dataset consists of 20 kinds of problems. In this study, artificial bee colony, particle swarm optimization, differential evolution, genetic and tree seed algorithm are used. In the experimental studies, five algorithms are set to run under equal conditions using the proposed synthetic dataset. When the acquired experimental results are examined, genetic algorithm seems to be the most effective algorithm in terms of both speed and performance. Although artificial bee colony has better results from genetic algorithm in a few problems, artificial bee colony is the second most successful algorithm after genetic algorithm in terms of performance. However, in terms of time, artificial bee colony is an algorithm nearly as successful as genetic algorithm. On the other hand, the results of differential evolution, particle swarm optimization and tree seed algorithms are similar to each other in terms of solution quality. In conclusion, the statistical tests clearly show that genetic algorithm is the most effective technique in solving LR problems in terms of speed, performance and robustness. (C) 2020 Elsevier B.V. All rights reserved.Article Citation - WoS: 6Citation - Scopus: 7A Novel Metaheuristic Algorithm by Efficient Crossover Operator for Land Readjustment(PERGAMON-ELSEVIER SCIENCE LTD, 2022-02-01) Koç, İsmail; Çay, Tayfun; Babaoğlu, İsmailLand readjustment and reallocation (LR) applications are complex and difficult real-world problems involving many different criteria. By considering these criteria, it is very difficult and takes a long time to be solved manually by an expert. Since the search space of these problems is very large, solution of these problems requires meta-heuristic optimization algorithms instead of classical methods in order to acquire more robust, acceptable and qualified solutions. Considering the meta-heuristic approaches, the algorithm needs an objective function that can make the right decision and evaluate the solutions most reasonably among the candidate solutions. Using the proposed objective function, the quality of the distribution and subdivision plans will be automatically evaluated and compared without the need for an expert. In this study, an objective function which considers all the criteria in the LR problems is proposed. In addition, unlike the available crossover operators used in metaheuristic algorithms in the literature, two different parcel-based crossover operators called Classical (CPC) and Intelligent (IPC) Parcel-Based Crossover Operators are proposed. While CPC performs the distribution of the owners to the predetermined parcel randomly, IPC makes this operation with a greedy approach rather than randomly. According to this approach, if the shareholder and distance values after the crossover operation would be better than the existing ones, the crossover operation is performed. Otherwise, this operation is cancelled. By using the proposed objective function and crossover operators, artificial bee colony (ABC), particle swarm optimization (PSO) and differential evolution (DE) algorithms are run under equal conditions on a real project site, and the obtained results are compared with the official results obtained by a technician in the study. In addition, since there will be so many zoning blocks of different sizes and shapes on a real project site, it is very possible to have gaps or overflows in the blocks of subdivision plans obtained from the algorithms. Therefore, the gaps and overflow areas in the blocks can be completely eliminated by utilizing an Expert System developed specifically for LR problems called LRES, and as a result, the solutions obtained from the algorithms can be directly applicable in real life by the LRES. It's clearly seen from the experimental studies that all of the results obtained by using the algorithms based on LRES are much more effective than the official results obtained by a technician in terms of both solution quality and speed. In addition, among the evaluated algorithms, it is observed that the PSO algorithm presents much more effective and robust results than results of the other algorithms. Moreover, as a consequence of the algorithms using the IPC presents much more successful results than the results of the algorithms using CPC, it can be used as a very effective alternative crossover operator for land use problems.Article TÜRKİYE'DE ENERJİ TALEBİNİ TAHMİN ETMEK İÇİN DOĞRUSAL FORM KULLANARAK GSA (YERÇEKİMİ ARAMA ALGORİTMASI) VE IWO (YABANİ OT OPTİMİZASYON ALGORİTMASI) TEKNİKLERİNİN UYGULANMASI(2018-12-01) Koç, İsmail; Nureddin, Refik; Kahramanlı, HumarBu çalışma, Türkiye'deki ekonomik göstergelere dayalı enerji talep tahmini ile ilgilidir. Enerji talebini tahmin etmek için Yerçekimi Arama Algoritması (GSA) ve Yabani Ot Algoritması (IWO) tekniklerine dayanan iki farklı model önerilmektedir. GSA yöntemi, Newton’un hareket ve yerçekimi kanunlarından esinlenerek geliştirilmiş sezgisel optimizasyon algoritmasıdır. IWO algoritması ise doğadaki yabani otların istilacı karakterlerinden esinlenen, evrimsel bir optimizasyon algoritmasıdır. GSA ve IWO yöntemlerine dayalı enerji talep modelleri, gayri safi yurtiçi hâsıla (GSYİH), nüfus, ithalat ve ihracat verilerini giriş parametresi şeklinde kullanan bir model olarak önerilmektedir. Önerilen yöntemler doğrusal regresyon modeli kullanılarak geliştirilmiştir. Türkiye’nin gelecekteki enerji talebi ise üç farklı senaryo altında tahmin edilmektedir. Önerilen tahmin modellerinden elde edilen deneysel sonuçlar karşılaştırmalı olarak verilmiştir. 1979 ve 2005 yılları arasındaki veriler kullanılarak gerçekleştirilen tahmin modelinde IWO literatürdeki diğer yöntemlerle de kıyaslanmış ve IWO yöntemi en yüksek performansı verdiği görülmüştür. 1979 ve 2011 yılları arasındaki tüm veri seti kullanılarak gerçekleştirilen tahmin modelinde ise GSA, IWO yöntemiyle karşılaştırılmış ve GSA daha iyi bir performans elde etmiştir.Article A Novel Crossover Based Discrete Artificial Algae Algorithm for Solving Traveling Salesman Problem(ZARKA PRIVATE UNIV, 2024) Nureddin, Refik; Koç, İsmail; Uymaz, Sait AliThe Artificial Algae Algorithm (AAA) is a newly proposed metaheuristic algorithm that is inspired by microalgae behaviors. This algorithm has been proposed for solving continuous optimization problems and achieved good results for the continuous problems. In addition, binary versions of AAA are proposed in the literature. This paper presents a discrete version of AAA, which is named Discrete Artificial Algae Algorithm (DAAA). For discretization of AAA, Crossover operators (one-point and uniform) are used in the processes (helical movement, evolutionary process, and adaptation). In this study, in addition to crossover operators, transformation operators such as swapping, insertion, symmetry, and reversion are also used. DAAA's ' s performance was analyzed on a well-known discrete optimization problem called the Traveling Salesman Problem (TSP). DAAA was tested on thirty-two Benchmark instances of the TSP. These instances were small-sized, medium-sized, and large-sized. Firstly, the AAA processes (evolutionary process, adaptation, and helical movement) with the combination of nearest neighbor and transformation operators were tested for selected benchmark instances and this testing was called Process Analysis. After this process Analysis the best processes with which to continue were selected, and after this decision comparisons with other algorithms were started. The main comparison is between discrete Social Spider Algorithm (DSSA) and DAAA, and DAAA outperformed DSSA on most of the problems. Further, DAAA's ' s performance on some of the benchmark instances was compared with some of the well-known algorithms for TSP. In this comparison, DAAA has achieved better results than many other algorithms. Experimental results show that DAAA has the capability of solving discrete optimization problems and outperforming other algorithms. .
Research Topics
Domains
Physical Sciences
Fields
MathematicsComputer SciencePhysics and Astronomy
Subfields
Modeling and SimulationInformation SystemsArtificial IntelligenceStatistical and Nonlinear Physics
Specific Research Areas
COVID-19 epidemiological studies
COVID-19 Digital Contact Tracing
Digital Education and Society
Metaheuristic Optimization Algorithms Research
Complex Network Analysis Techniques
Sustainable Development Goals
15LIFE ON LAND
3
Research Products
11SUSTAINABLE CITIES AND COMMUNITIES
3
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
2
Research Products
7AFFORDABLE AND CLEAN ENERGY
1
Research Products
Documents
14
Citations
288
h-index
9
Documents
14
Citations
236
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Konya Technical University | 12 |
| Selçuk University | 9 |
| Şırnak University | 2 |
| Sakarya University | 1 |
| Tokat Gaziosmanpaşa Üniversitesi | 1 |
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Data obtained from OpenAlex
| Journal | Count |
|---|---|
| Engineering Applications of Artificial Intelligence | 2 |
| Mühendislik Bilimleri ve Tasarım Dergisi | 2 |
| Celal Bayar Üniversitesi Fen Bilimleri Dergisi | 1 |
| COMPUTER SYSTEMS SCIENCE AND ENGINEERING | 1 |
| Expert Systems With Applications | 1 |
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Scholarly Output
20
Articles
15
Views / Downloads
54/108
Supervised MSc Theses
2
Supervised PhD Theses
1
WoS Citation Count
70
Scopus Citation Count
85
Patents
0
Projects
0
WoS Citations per Publication
3.50
Scopus Citations per Publication
4.25
Open Access Source
11
Supervised Theses
3
Scopus Quartile Distribution
Competency Cloud
