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An idea based on honey bee swarm for numerical optimization
Dervis Karaboga
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The article was published on 2005-01-01 and is currently open access. It has received 5265 citations till now. The article focuses on the topics: Artificial bee colony algorithm & Bees algorithm.read more
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Book ChapterDOI
Introduction to Algorithms
TL;DR: This chapter provides an overview of the fundamentals of algorithms and their links to self-organization, exploration, and exploitation.
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
Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems
TL;DR: The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort and results show that TLBO is more effective and efficient than the other optimization methods.
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.
Journal ArticleDOI
A comparative study of Artificial Bee Colony algorithm
Dervis Karaboga,Bahriye Akay +1 more
TL;DR: Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.
References
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Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
BookDOI
Swarm intelligence: from natural to artificial systems
TL;DR: This chapter discusses Ant Foraging Behavior, Combinatorial Optimization, and Routing in Communications Networks, and its application to Data Analysis and Graph Partitioning.
Journal ArticleDOI
Swarm Intelligence: From Natural to Artificial Systems
TL;DR: This book provides fairly comprehensive coverage of recent research developments and constitutes an excellent resource for researchers in the swarm intelligence area or for those wishing to familiarize themselves with current approaches e.g. it would be an ideal introduction for a doctoral student wanting to enter this area.
Book ChapterDOI
Reaction-Diffusion Model of a Honeybee Colony's Foraging Behaviour
TL;DR: The results elucidate the role of natural clustering of the dances in the small area of the have - it has to facilitate the information flow that is beneficial for overall process of colony's food collection.