Abstract
The artificial bee colony optimization (ABC) is a population based algorithm for function optimization that is inspired by the foraging behaviour of bees. The population consists of two types of artificial bees: employed bees (EBs) which scout for new good solution in the search space and onlooker bees (OBs) that search in the neighbourhood of solutions found by the EBs. In this paper we study the influence of the populations size on the optimization behaviour of ABC. Moreover, we investigate when it is advantageous to use OBs. We also propose two variants of ABC which use new methods for the position update of the artificial bees. Empirical tests were performed on a set of benchmark functions. Our findings show that the ideal population size and whether it is advantageous to use OBs depends on the hardness of the optimization goal. Additionally the newly proposed variants of the ABC outperform the standard ABC significantly on all test functions. In comparison to several other optimization algorithm the best ABC variant performs better or at least as good as all reference algorithms in most cases.
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Aderhold, A., Diwold, K., Scheidler, A., Middendorf, M. (2010). Artificial Bee Colony Optimization: A New Selection Scheme and Its Performance. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol 284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12538-6_24
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DOI: https://doi.org/10.1007/978-3-642-12538-6_24
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