A comprehensive survey: artificial bee colony (ABC) algorithm and applications

By Dervis Karaboga, Beyza Görkemli, Celal Öztürk, N. Karaboga. Artificial Intelligence Review, 2012

1,844 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b96b75202cacf10f693c5294da709b9a7823c0af

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Citations in the corpus

Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems (2020, background): “Amongst some of the most successful metaheuristic algorithms are simulated annealing (SA) [11], particle swarm optimization (PSO) [12], genetic algorithms (GA) [13], ant colony optimization (ACO) [14], and artificial bee colony (ABC) [15].”
Frequency Diverse Array Antenna Using Time-Modulated Optimized Frequency Offset to Obtain Time-Invariant Spatial Fine Focusing Beampattern (2016, methodology): “The ABC algorithm is preferred because it is simple, flexible, and robust in comparison with other swarm based algorithms [37]–[39].”
Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey (2018, methodology): “ABC algorithm is used in solving numerous real world problems in various fields and successful outcomes are obtained (Akay and Karaboga 2015; Karaboga et al. 2014; Kockanat and Karaboga 2015a, b; Koza and Karaboga 2017; Ozdemir and Karaboga 2017).”
Optimizing connection weights in neural networks using the whale optimization algorithm (2016, background): “2006), and the artificial bee colony (ABC) (Karaboga et al. 2014; Karaboga 2005).”
Hybrid clustering analysis using improved krill herd algorithm (2018, methodology): “The ABC is one of the most recently introduced metaheuristic optimization algorithms [52].”
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems (2015, background): “Since the proposal of these algorithms, a significant number of researchers attempted to improve or apply them in to different problems in diverse fields [15–20].”
A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, methodology): “Surveys of the different developments can be found in [103, 104, 105].”
A Comprehensive Review of Swarm Optimization Algorithms (2015, neutral): “ABC has been implemented in various fields including engineering design problems [82, 83], networking [84], business [85], electronics [86], scheduling [86] and image processing [86].”
Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm (2016, methodology): “EAs differ from some optimization methods, such as Simulated-Annealing [2] and Tabu search [3], because they evolve a population of solutions to reach an approximate optimal solution instead of one solution [4].”
Bio inspired computing - A review of algorithms and scope of applications (2016, methodology): “The behaviour of honey bees based on the communication, task allocation, nest site selection, reproduction, mating, floral foraging, and pheromone laying and navigation behaviours has been mimicked in this algorithm (Karaboga et al., 2014).”