An artificial bee colony (ABC) algorithm for numeric function optimization

By Bahriye Basturk. 2006

533 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4fccec155f546b349ab84a6f5cee80e6bbb2b49b

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Citations in the corpus

A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, methodology): “The major such algorithms include: Ant colony optimization (ACO) by Dorigo [62] in 1992, Particle swarm optimization (PSO) by Kennedy and Eberhart in 1995 [68,69], Artificial bee colony (ABC) algorithm by Karaboga and Basturk in 2006 [73], Fish Swarm Algorithm (FSA) by Li et al.”
On the performance of artificial bee colony (ABC) algorithm (2008, methodology): “This work compares the performance of ABC algorithm with that of DE and PSO algorithms, and EA for a set of well-known test functions.”
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems (2007, methodology): “Karaboga and Basturk have compared the performance of the ABC algorithm with those of other well-known modern heuristic algorithms such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) on unconstrained problems [12].”
A comparative study of Artificial Bee Colony algorithm (2009, methodology): “Basturk and Karaboga compared the performance of ABC algorithm with those of GA [27], PSO and PS-EA [28]; and DE, PSO and EA [29] on a limited number of test problems.”
A survey on new generation metaheuristic algorithms (2019, methodology): “Karaboga and Basturk (2008) compare the performance of ABC algorithm with DE, PSO and GA for multi-dimensional numeric problems.”
Grey Wolf Optimizer (2014, background): “Some of the most popular SI techniques are ACO [2], PSO [3], and Artificial Bee Colony (ABC) [7].”
Artificial bee colony algorithm for large-scale problems and engineering design optimization (2010, methodology): “…by Karaboga (2005) simulates the foraging behaviour of a bee colony and it was successfully applied to optimize numerical problems (Karaboga 2005; Basturk and Karaboga 2006; Karaboga and Basturk 2007b, 2008; Karaboga and Akay 2007; Karaboga et al. 2007; Karaboga and Basturk 2007a; Karaboga and…”