Abstract
The Bees Algorithm models the foraging behaviour of honey bees in order to solve optimisation problems. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This paper describes the Bees Algorithm, and compares its functioning and performance with those of other state-of-the-art nature-inspired intelligent optimisation methods. Two application cases are presented: the minimisation of a set of well-known benchmark functions, and the training of neural networks to reproduce the inverse kinematics of a robot manipulator. In both cases, the Bees Algorithm proved its effectiveness and speed. Compared with other state-of-the-art methods, the performance of the Bees Algorithm was very competitive.
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Pham, D.T., Castellani, M., Le Thi, H.A. (2014). Nature-Inspired Intelligent Optimisation Using the Bees Algorithm. In: Nguyen, NT., Le-Thi, H.A. (eds) Transactions on Computational Intelligence XIII. Lecture Notes in Computer Science, vol 8342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54455-2_2
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