Browsing by Author "Erdogdu, Kazim"

Now showing 1 - 5 of 5

Citation - WoS: 1
Citation - Scopus: 1
A 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 Costanzo
Recent 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.
Citation - WoS: 8
Citation - Scopus: 10
An 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, Korhan
Concept 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.
Citation - WoS: 19
Citation - Scopus: 22
Bi-objective green vehicle routing problem
(WILEY, 2022) Kazim Erdogdu; Korhan Karabulut; Erdogdu, Kazim; Karabulut, Korhan
The 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.
Citation - WoS: 4
Citation - Scopus: 5
Distance 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, Korhan
As 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.
Citation - Scopus: 1
Self-Adaptive Genetic Algorithm For Permutation Flow Shop Scheduling Problems
(Institute of Electrical and Electronics Engineers Inc., 2023) Cihanser Çaliskan; Kazım Erdoǧdu; Erdogdu, Kazim; Çaliskan, Cihanser
The 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.