Erdoğdu, Kazim
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Dr.Öğr.Üyesi
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01.01.09.07. Yazılım Mühendisliği Bölümü
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2ZERO HUNGER
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3GOOD HEALTH AND WELL-BEING
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4QUALITY EDUCATION
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
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7AFFORDABLE AND CLEAN ENERGY
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8DECENT WORK AND ECONOMIC GROWTH
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
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39
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Scopus Citations per Publication
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| Journal | Count |
|---|---|
| 10th International Conference on Electrical and Electronics Engineering ICEEE 2023 | 1 |
| 5th World Conference on Information Systems and Technologies (WorldCIST) | 1 |
| 7th International Conference on Electrical and Electronics Engineering ICEEE 2020 | 1 |
| Deu Muhendislik Fakultesi Fen ve Muhendislik | 1 |
| Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi | 1 |
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Article Trafik Sıkışıklığı Olan Gezgin Satıcı Probleminde Karınca Kolonisi Optimizasyonu ve Işın-Karınca Kolonisi Optimizasyonu(2024) Orçun, Mustafa; Erdoğdu, KazımThe Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem that has various implications in a variety of industries. Even the purest formulation of TSP has applications on from logistics routes to microchip manufacturing, unexpectedly, it can be used on DNA sequencing with slight modification as a sub-problem. In this paper, two versions of TSP were studied, a classical TSP and the TSP containing traffic congestion data. Two state-of-the-art solution methods were used, Ant Colony Optimization (ACO) and Beam-ACO. These algorithms were hybridized with 2-Opt local search and their performances compared on the same benchmark instances. The experimental results show the efficiency of Beam-ACO compared to ACO.Conference Object Citation - WoS: 1Citation - Scopus: 1A Feature Selection Application Using Particle Swarm Optimization for Learning Concept Detection(SPRINGER-VERLAG BERLIN, 2017) Korhan Gunel; Kazim Erdogdu; Refet Polat; Yasin Ozarslan; Polat, Refet; Erdogdu, Kazim; Ozarslan, Yasin; Gunel, Korhan; A Rocha; AM Correia; H Adeli; LP Reis; S CostanzoRecent developments of computational intelligence on educational technology yield concept map mining as a new research area. Concept map mining covers the extraction of learning concepts specifying relations among them and generating a concept map from educational contents. In this study we focused on determining the features that characterize a learning concept extracted from an educational text as raw data. The first three features are detected by using a hybrid system of Multi Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) and the performance of the applied method is gauged in the viewpoint of a typical classification problem.Article Citation - WoS: 19Citation - Scopus: 22Bi-objective green vehicle routing problem(WILEY, 2022) Kazim Erdogdu; Korhan Karabulut; Erdogdu, Kazim; Karabulut, KorhanThe green vehicle routing problem (GVRP) is a variant of the vehicle routing problem (VRP) which increasingly attracts many researchers in recent years due to the growing global environmental issues. As the transportation of the products grows the number of vehicles in fleets and the pollutants caused by these vehicles also grow which in turn negatively affects human health. In this paper a biobjective GVRP was studied. The two objectives are minimizing the total distance and minimizing the total fuel consumption of all vehicle routes. As a solution method an adaptive large neighborhood search was hybridized with two new local search heuristics. The proposed method was applied to two well-known benchmark problem sets for VRPs and new approximate Pareto fronts were obtained for these benchmark sets.Article Çok Bölmeli Araç Rotalama Problemi için Bir Melez Genetik Algoritma(2021) Kazım Erdoğdu; Erdoğdu, KazımBu çalışmada Çok Bölmeli Araç Rotalama Problemi (ÇB-ARP) ele alınmıştır. Günlük hayattamarketler firmalar ve kurumlar bazı ürünleri müşterilerine teslim ederken ya da belirli noktalardantoplarken bu ürünleri araç içinde farklı bölmelere koymaları gerekmektedir. Bazı ürünlerin odasıcaklığında bazılarının soğuk olarak taşınması gerekmektedir. Bazı atıkların kimyasal ürünlerin yada yakıtların diğer ürünlerle karıştırılmadan taşınması gerekmektedir. Bu yüzden dağıtım ya datoplama yapan araç filosundaki her bir aracın birden fazla bölmeye sahip olması ve dağıtılan ya datoplanan ürünlerin ilgili bölmelerde taşınması gerekmektedir. Bu makalede çalışılan ÇB-ARP bir ikive üç bölmeli araç senaryoları dahilinde ayrı ayrı ele alınmıştır. Çözüm yöntemi olarak melez birGenetik Algoritma (GA) kullanılmış ve bu algoritma Araç Rotalama Problemi (ARP) literatüründesıklıkla kullanılan bir problem örnek seti üzerinde uygulanmıştır. Sonuç olarak bu çalışmadaki ÇBARPmodeli için yeni referans sonuçları üretilmiş ve sonuçlar yorumlanmıştır.Article Citation - WoS: 8Citation - Scopus: 10An empirical study on evolutionary feature selection in intelligent tutors for learning concept detection(WILEY, 2019) Korhan Gunel; Kazim Erdogdu; Refet Polat; Yasin Ozarslan; Polat, Refet; Erdogdu, Kazim; Ozarslan, Yasin; Gunel, KorhanConcept map mining (CMM) has emerged as a new research area with recent developments in computational intelligence in educational technology. CMM includes the following steps: extracting the learning concepts from educational content specifying relations among them and generating a concept map as a result. The purpose of this study was to develop a mechanism using data mining technique to determine the features that characterize a learning concept extracted automatically from a single educational text. The 3 major features that distinguish the real learning concepts from other sequences of strings are detected by using a hybrid system of a feed-forward neural network and some evolutionary algorithms. Ant colony optimization and genetic algorithm and particle swarm optimization are used as a binary feature selection method. In addition the aforementioned methods are hybridized to get better accuracy and precision. The performance comparisons with two different state-of-the-art algorithms have been made from the viewpoint of a typical classification problem.Conference Object Citation - Scopus: 1Self-Adaptive Genetic Algorithm For Permutation Flow Shop Scheduling Problems(Institute of Electrical and Electronics Engineers Inc., 2023) Cihanser Çaliskan; Kazım Erdoǧdu; Erdogdu, Kazim; Çaliskan, CihanserThe permutation flow shop scheduling problem (PFSSP) is a well-known extensively researched and heavily applied non-polynomial (NP)-Hard combinatorial optimization problem. It is encountered in various real-life manufacturing problems such as automotive manufacturing integrated circuit fabrication and agricultural food industries. It continues to gain popularity in operational research areas as new manufacturing areas are developed. Therefore finding a solution to these NP-Hard problems attract the attention of scientists. In this paper we studied a PFSSP and proposed a new heuristic for its solution: The Self-Adaptive Genetic Algorithm (GA). This proposed algorithm uses a conventional GA with cycle crossover and random swap mutation. Its novelty on the other hand lies in incorporating an adaptive mechanism in the GA. The proposed algorithm uses three different local searches (i.e. 2-Opt Greedy Insert and Greedy Swap local searches) based on their successes. In other words the proposed algorithm evaluates the performance of each local search at each generational iteration and makes a decision on which one to use based on their previous performances. The more successful local searches increase their probability of selection and vice versa. This way Self-Adaptive GA hence the name adapts and directs its exploitation by the information it obtains in its previous generations. The proposed algorithm was applied to a subset of well-known Taillard problem instances. The experimental studies show its successful performance. Self-Adaptive GA obtained the optimum results for 7 out of 18 benchmark instances. In the rest of the 11 instances the differences between the results of the proposed method and the optimum values are less than 2%. © 2023 Elsevier B.V. All rights reserved.Article A new model for minimizing the electric vehicle battery capacity in electric\rtravelling salesman problem with time windows(Tubitak Scientific & Technological Research Council Turkey, 2021) Kazım Erdoğdu; KORHAN KARABULUT; Erdoğdu, Kazım; Karabulut, KorhanThe growing pollution in the environment and the negative shift in the global climate compel authorities\rto take action to protect the environment and human health. Transportation is one of the major contributors to this\renvironmental decay. The harmful gases released to the air by the vehicles using petroleum fuel increase each day. One\rof the solutions is to make a gradual transition to electric vehicles. A major part of manufacturing an electric vehicle\ris to produce an efficient electric motor and battery for it. Reducing the manufacturing and operating costs of these\rcomponents will result in reducing the overall costs of electric vehicles. In this study a new variant of the electric\rtravelling salesman problem with time windows (E-TSPTW) was proposed. The objective function of the problem is to\rminimize the required initial battery capacity of the electric vehicle. For this goal a new energy consumption model\rconsidering the load of the vehicle was proposed with three scenarios. The proposed model was solved with a hybrid\rsimulated annealing algorithm for all these scenarios. The performance of the proposed method was compared to the\rsolutions found by a mixed integer linear programming model. The experimental results on the benchmark instances\rshow that up to a 35% reduction in initial battery capacity hence reduction in its cost is possible.\rConference Object Citation - WoS: 4Citation - Scopus: 5Distance and Energy Consumption Minimization in Electric Traveling Salesman Problem with Time Windows(Institute of Electrical and Electronics Engineers Inc., 2020) Kazım Erdoǧdu; Korhan Karabulut; Erdogdu, Kazim; Karabulut, KorhanAs global pollution caused by transportation increases the need for cleaner energy becomes more significant each day. For this reason one of the recent global technological and scientific tendencies is to develop and include electric vehicles in transportation. In this paper an Electric Traveling Salesman Problem with Time Windows was studied by considering two objectives: minimizing the total distance and minimizing the total energy consumption. As a solution method the well-known Simulated Annealing algorithm was hybridized with a constructive heuristic and a local search heuristic. This algorithm was executed on a set of well-known benchmark instances from the literature separately for the two objectives and the results were presented. © 2020 Elsevier B.V. All rights reserved.
