New ideas in optimization
Reaction-Diffusion Model of a Honeybee Colony's Foraging Behaviour
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
New inspirations in swarm intelligence: a survey
International Journal of Bio-Inspired Computation
Opposition-based artificial bee colony algorithm
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Performance assessment of foraging algorithms vs. evolutionary algorithms
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Adaptive filtering noisy transcranial Doppler signal by using artificial bee colony algorithm
Engineering Applications of Artificial Intelligence
Pilot Tones Optimization Using Artificial Bee Colony Algorithm for MIMO---OFDM Systems
Wireless Personal Communications: An International Journal
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While solving a problem by an optimization algorithm, adjusting algorithm parameters have significant importance on the performance of the algorithm. A fine tuning of control parameters is required for most of the algorithms to obtain desired solutions. In this study, performance of the Artificial Bee Colony (ABC) algorithm, which simulates the foraging behaviour of a honey bee swarm, was investigated by analyzing the effect of control parameters.