B
Bahriye Basturk
Researcher at Erciyes University
Publications - 5
Citations - 10839
Bahriye Basturk is an academic researcher from Erciyes University. The author has contributed to research in topics: Metaheuristic & Multi-swarm optimization. The author has an hindex of 3, co-authored 5 publications receiving 9296 citations.
Papers
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Journal ArticleDOI
A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm that is used for optimizing multivariable functions and the results showed that ABC outperforms the other algorithms.
Journal ArticleDOI
On the performance of artificial bee colony (ABC) algorithm
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: The simulation results show that the performance of ABC algorithm is comparable to those of differential evolution, particle swarm optimization and evolutionary algorithm and can be efficiently employed to solve engineering problems with high dimensionality.
Book ChapterDOI
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: The ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems to show superior performance on these kind of problems.
Proceedings ArticleDOI
Image segmentation using differential evolution algorithm
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: The abilities of differential evolution optimization algorithm, such as easiness, simple operations using, effectiveness and converging to global optimum reflected to image segmentation are reflected by using differential evolution algorithm in image segmentsation.
Diferansiyel Gelişim Algoritmasi ile İmge Bölütleme Image Segmentation using Differential Evolution Algorithm
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: The abilities of differential evolution optimization algorithm, such as easiness, simple operations using, effectiveness and converging to global optimum reflected to image segmentation are reflected by using differential evolution algorithm in image segmentsation.