Mühendislik Fakültesi

Permanent URI for this collection

http://dspace.yalova.edu.tr/handle/1/19

Browse

Browsing Mühendislik Fakültesi by Subject "artificial bee colony"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Artificial Bee Colony Algorithm Based Hyper-Parameter Optimization for Convolutional Neural Networks
    (Yalova University, 2021-05-21) Özdemir, Koray; Özen, Yunus; Tuncer, Adem
    In recent years, Deep Learning has become a field that researchers are particularly interested in. The Convolutional Neural Network (CNN) is a type of multi-layered artificial neural network mostly used in the analysis, recognition, and classification of images and videos. The performance of CNN models is usually based on custom model architectures, thus several hyper-parameter values in a CNN are manually selected mostly. However, different combinations of hyper-parameters and models need to be used to achieve better performance results. Determination of optimum values of hyper parameters is also an optimization problem. The metaheuristic optimization techniques are able to solve such problems. In this paper, we propose to use the Artificial Bee Colony (ABC) algorithm which is a meta-heuristic approach to automatically determine the optimum architecture of a CNN by means of hyper-parameters. The most effective hyperparameters in the performance of CNN models have been optimized, which are the number of layers, the number and size of filters, activation function, batch size, learning rate, optimizer, and dropout rate. We have evaluated our optimized architecture using the well-known Fashion-MNIST dataset. The results demonstrate that the proposed model using ABC improves the performance of a CNN model.
  • Thumbnail Image
    Item
    Crew Scheduling Optimization with Artificial Bee Colony Algorithm
    (8th International Advanced Technologies Symposium (IATS’17), 2017-10-19) Sardoğan, Melike; Tuncer, Adem
    Crew scheduling is one of the most important optimization problems for airline companies. It is the scheduling of weekly or monthly work schedule under certain constraints, such as working hours and weekly permits. There are many studies using analytical and heuristic approaches in the literature in order to solve this problem. In studies using heuristic approaches, genetic algorithms are used frequently. In this study, an artificial bee colony algorithm, which is a heuristic method, is used instead of the approaches applied to the current problem. Weekly work schedules are optimized according to daily working hours and days off for crew scheduling under a number of different personnel. From the simulation results, it is clearly seen that the artificial bee colony algorithm produces successful results within reasonable time.