A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

By Dervis Karaboga, Bahriye Basturk. Journal of Global Optimization, 2007

7,637 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/4cb5dd388ea707bd421226d8c1ef61e7f56982e2

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Citations in the corpus

A comprehensive survey on recent metaheuristics for feature selection (2022, background): “The behaviors of a bee colony are computationally represented as follows [116].”
Genetic Learning Particle Swarm Optimization (2016, methodology): “In this section, we further compare GL-PSO with other evolutionary and swarm intelligence algorithms, including a continuous ant colony optimization (ACOR) [51], self-adaptive DE (SaDE) [52], EDA [53], artificial bee colony (ABC) [54], and evolution strategy with covariance matrix adaptation (CMA-ES”
Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms (2012, methodology): “Artificial bee colony (ABC) algorithm Artificial bee colony (ABC) algorithm proposed by Karaboga (2005) and further developed by Karaboga and Basturk (2007, 2008) is a nature inspired algorithm based on the intelligent foraging behaviour of a honey bee swarm.”
A survey: algorithms simulating bee swarm intelligence (2009, methodology): “Basturk and Karaboga compared the performance of ABC algorithm with that of GA Basturk and Karaboga 2006, PSO and PS-EA Karaboga and Basturk 2007b; and DE, PSO and EA Karaboga and Basturk 2008; Karaboga and Akay 2008b on a set of numerical test problems.”
Binary grey wolf optimization approaches for feature selection (2016, background): “The onlooker bees follow their interesting employer, and the scout bee fly spontaneously to find the best food source [9].”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “In the ABC algorithm, the probability values are calculated using the fitness values (see, Eq. 6 of Karaboga and Akay 2009a ,E q.3 of Akay and Karaboga 2010 and Eq. 2.1 of Karaboga and Basturk 2007b ).”
Gbest-guided artificial bee colony algorithm for numerical function optimization (2010, methodology): “As well known that both exploration and exploitation are necessary for the population-based optimization algorithms, such as GA [1,2], PSO [3,4], DE [12], and so on.”
A Review of the Artificial Neural Network Models for Water Quality Prediction (2020, background): “The convergence rate and the solution accuracy could be improved through the variant of PSO (IPSO).”
Oppositional biogeography-based optimization (2009, methodology): “This flavor of the DE algorithm is commonly referred as DE/rand/1/bin [157] and is outlined in Algorithm 5.”