AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION

By Dervis Karaboga. 2005

6,487 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cf20e34a1402a115523910d2a4243929f6704db1

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Citations in the corpus

Gbest-guided artificial bee colony algorithm for numerical function optimization (2010, background): “A set of experimental results on function optimization [8–11] show that ABC algorithm is competitive with some conventional biological-inspired optimization algorithms, such as GA, differential evolution (DE) [12], and PSO.”
A review: On path planning strategies for navigation of mobile robot (2019, background): “20) in search of food and is proposed by Kharaboga [175].”
Salp swarm algorithm: a comprehensive survey (2019, methodology): “…algorithm [17], symbiotic organisms search algorithm [18], dragonﬂy algorithm [19], cuckoo search algorithm [20], artiﬁcial bee colony algorithm [21], ﬂower pollination algorithm [22], moth-ﬂame optimization algo-rithm [23], bacterial foraging algorithm [24], biogeogra-phy-based optimization…”
A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, methodology): “Artificial bee colony (ABC) [7] Continuous”
A swarm optimization algorithm inspired in the behavior of the social-spider (2013, background): “Interactions among members are either direct or indirect [25].”
Fitness Function in ABC Algorithm for Uncapacitated Facility Location Problem (2015, methodology): “The fitness value fitness(xk) of the potential solution of the employed bee k is calculated from its total cost xk by the formula below that is given in [4].”
Swarm Intelligence Algorithms for Feature Selection: A Review (2018, methodology): “ABC Artificial Bee Colony Algorithm [112] ABO African Buffalo Optimization [113] ACO Ant Colony Optimization [8] AFSA Artificial Fish Swarm Algorithm [114] ALO Antlion Optimizer [115] BA Bat Algorithm [116] BA* Bees Algorithm [117] BA** Beaver Algorithm [118] BBMO Bumble Bees Mating Optimization [11”
Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning (2014, methodology): “In recent years, there are a lot of population-based swarm intelligence algorithms existed, such as ant colony optimization, particle swarm optimization (Kennedy and Eberhart, 1995), artificial bee colony algorithm (Karaboga, 2005; Karaboga and Basturk, 2007), imperialist competitive algorithm (Esma”
Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems (2012, unknown): “2001), bacteria foraging optimization (BFO) (Passino 2002), shufﬂed frog leaping (SFL) (Euusuff and Lansey 2003), artiﬁcial bee colony (ABC) (Karaboga 2005), biogeography-based optimization (BBO) (Simon 2008), gravitational search algorithm (GSA) (Rashedi et al. 2009) and grenade explosion method (G”
Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey (2018, methodology): “ABC algorithm, which was developed by Karaboga in 2005, models the food searching behaviour of the honey bees (Karaboga 2005; Karaboga and Basturk 2007; Karaboga 2010).”
EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019, methodology): “optimization [3,95], artificial bee colony (ABC) optimization [3,96], ant colony optimization (ACO) [3], genetic algorithms (GAs) [49] and the firefly algorithm [74] have been successfully applied for feature selection and reduction.”
Bio-inspired computation: Where we stand and what's next (2019, background): “Ensembles of multi-strategy approaches based on different algorithms have been proposed in the literature, for example, based on DE [252,253], PSO [254], Artificial Bee Colony algorithms [255] or Biogeography-based optimization [256].”
An artificial bee colony algorithm for the capacitated vehicle routing problem (2011, methodology): “While the above heuristics have been widely used and successfully applied to solve the CVRP for several years, Artificial Bee Colony (ABC) is a fairly new approach introduced just a few years ago by Karaboga (2005), but has not yet been applied to solve the CVRP.”