A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligenc...

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Bibliographic Details
Published inJournal of global optimization Vol. 39; no. 3; pp. 459 - 471
Main Authors Karaboga, Dervis, Basturk, Bahriye
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Nature B.V 01.11.2007
Subjects
Algorithms
Artificial intelligence
Bees
Birds
Chromosomes
Food
Foraging behavior
Genetic algorithms
Heuristic
Immune system
Intelligence
Mathematical models
Mutation
Optimization
Optimization algorithms
Optimization techniques
Population
Robots
Simulation
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ISSN0925-5001
1573-2916
DOI10.1007/s10898-007-9149-x

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Summary:Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms. [PUBLICATION ABSTRACT]
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ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-007-9149-x