Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems

By Dervis Karaboga, Bahriye Basturk. International Fuzzy Systems Association World Congress, 2007

1,386 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/9aecdddc96bc832829ba35356afa1f10ea0a8930

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In Industrial and Manufacturing Engineering

Citations in the corpus

Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems (2019, background): “Colony (ABC) algorithm [36] which is inspired by the collective behavior of bees to find the food sources.”
Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems (2011, neutral): “This problem was solved by the different optimization methods: Multimem-bered Evolutionary Strategy (M-ES) [14], Particle Evolutionary Swarm Optimization (PESO) [15], Cultural Differential Evolution (CDE) [16], Co-evolutionary Differential Evolution (CoDE) [17] and Artificial Bee Colony (ABC) [18].”
Gbest-guided artificial bee colony algorithm for numerical function optimization (2010, background): “parameter optimization of milling process [18], the optimization of constrained problems [19], the lot-streaming flow shop scheduling problem [20], and so on.”
Artificial bee colony algorithm for large-scale problems and engineering design optimization (2010, methodology): “The Artificial Bee Colony algorithm proposed 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 Basturk2007b,2008;KarabogaandAkay2007;Karabogaet al. 2007; Karaboga a”
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, methodology): “Finally, well-known methods such as Artificial Bee Colony [76] and Cuckoo Search (CS, [77]) are characterized for being multi-agent, meaning that each individual of the community can behave differently.”
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications (2015, background): “Basturk [10] proposed artificial bee colony (ABC), which simulates the foraging behavior of honey bees.”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “the benchmark functions can be found in (Karaboga and Akay 2009a; Karaboga and Basturk 2007a, b).”
Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems (2022, background): “Artificial bee colony (ABC) [29] algorithm is another important algorithm that mimics the social behavior of honeybees.”
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.”