A survey: algorithms simulating bee swarm intelligence

By Dervis Karaboga, Bahriye Akay. Artificial Intelligence Review, 2009

702 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/306c7ad3badd0eb652080a63c2dc814020d36299

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

A survey on optimization metaheuristics (2013, background): “A recent literature survey of the algorithms inspired by bees’ behavior in the nature and their applicati ons is given in [143].”
A comprehensive survey: artificial bee colony (ABC) algorithm and applications (2012, background): “The first comprehensive survey on the algorithms related to the bee SI and their applications was prepared by Karaboga and Akay (2009c).”
New inspirations in swarm intelligence: a survey (2011, background): “A recent work (Karaboga and Akay, 2009a) compared the ABC algorithm performance against other population-based algorithms (genetic algorithm, particle swarm optimisation, differential evolution and evolution strategies) upon several benchmark functions.”
A social learning particle swarm optimization algorithm for scalable optimization (2015, background): “Behavior learning is an essential and common process for updating particle behaviors, which is also similar to other learning mechanisms in various swarm intelligence algorithms [18,40,43,59].”
Sensor Deployment and Scheduling for Target Coverage Problem in Wireless Sensor Networks (2014, methodology): “Algorithm 4 PSO Algorithm 1: Initialize particles 2: repeat 3: for each particle do 4: Calculate the fitness value 5: if fitness value is better than the best fitness value (pbest) in history then 6: Set current value as the new pbest 7: end if 8: end for 9: Choose the particle with the best fitness”
A Comprehensive Survey of Clustering Algorithms (2015, background): “For more detailed information about this kind of clustering algorithms, you can refer to [102–104].”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “The methods which predicate on the collective search of the patterns for the solutions of the related problem are generally known as the artificial swarm intelligence methods (Dorigo et al. 2000, 2004; Martinoli et al. 2004; Sousa et al. 2004; Mahamed and Mehrdad 2008; Karaboga and Akay 2009b).”