Artificial Bee Colony Algorithm

Artificial Bee Colony Algorithm

Ömür Tosun (Akdeniz University, Turkey)
Copyright: © 2014 |Pages: 14
DOI: 10.4018/978-1-4666-5202-6.ch018
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Background

Swarm intelligence is one of the popular research topics which has inspired from nature, mostly living biological systems. The expression was introduced by Gerardo Beni and Jing Wang in 1989, with the context of cellular robotic systems. Some of its natural examples are ant colonies, bacterial growth, animal herding, bird flocking and fish schooling.

Key Terms in this Chapter

Artificial Intelligence: Intelligent searching methods (learning algorithms, neural networks…) used for optimization.

Swarm Intelligence: Collective behaviors of ants, fishes, birds, bees and other social instincts or animals.

Optimization: Using different solution methods to find best or near optimal solutions (with minimum cost or maximum performance) under the effect of controllable factors (inputs and outputs) in an acceptable time for quantitative problems.

Metaheuristic: A computational method to find optimal solutions by iteratively improving candidate solutions under a pre-determined measure of quality.

Artificial Bee Colony Algorithm: A swarm intelligence based optimization algorithm based on foraging behavior of honey bees.

Self Organization: Self organization is the key feature which gives the collective behavior of the simple units in a swarm.

Bio-Inspired Algorithms: Nature inspired algorithms used for solving problems that imitate the way nature performs (strategy of nature).

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