On the performance of artificial bee colony (ABC) algorithm

By Dervis Karaboga, Bahriye Basturk. Applied Soft Computing, 2008

3,773 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/20ae8aebf1f0f7e85f2548784a9543803a4db199

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In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

A Sustainable Road Network Design Problem with Land Use Transportation Interaction over Time (2015, background): “In general, ABC belongs to a class of evolutionary algorithms, and are inspired by the intelligent behavior of the honeybees in finding nectar sources around their hives (Karaboga and Basturk 2008).”
Artificial bee colony algorithm for large-scale problems and engineering design optimization (2010, methodology): “…(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 Akay 2009).”
A multiscale flaw detection algorithm based on XFEM (2014, methodology): “An optimal value D 0:3 is used in this paper, which is obtained from the continuous numerical optimization test of five benchmark functionals as used in [28].”
Chaotic bee colony algorithms for global numerical optimization (2010, methodology): “Recently proposed artificial bee colony (ABC) algorithm has been inspired by the intelligent behavior of real honey bees and proven to be a better heuristic for global numerical optimization (Karaboga, 2005; Karaboga & Basturk, 2008).”
Modeling the formation and growth of atmospheric molecular clusters: A review (2020, methodology): “ABCluster, which uses the Artificial Bee Colony algorithm (Karaboga & Basturk, 2008), has already been utilized in many configurational sampling procedures of atmospherically relevant molecular clusters (Chen et al., 2020a; Hou et al., 2017; Liu, Li et al., 2018; Malloum et al., 2019; Myllys, Chee…”
Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms (2012, methodology): “This operation can be expressed as, x i = x min + rand(0, 1)(x max − x min) (23) The control parameters used in ABC are number of food sources which is equal to number of employed bees, number of onlookers and scout bees, the value of predetermined number of cycles which is called ‘limit’ and genera”
Nondestructive identification of multiple flaws using XFEM and a topologically adapting artificial bee colony algorithm (2013, methodology): “It has also been compared with other various heuristic algorithms [26, 27].”
A new metaheuristic for numerical function optimization: Vortex Search algorithm (2015, background): “Ant Colony Optimization (ACO) [12], Particle Swarm Optimization (PSO) [29] and Artificial Bee Colony algorithm (ABC) [5], [25], [26], [27] are well-known examples of the swarm-based algorithms.”