Abstract
We modify an artificial bee colony algorithm as follows: we make the population size grow over time and apply local search on strategically selected solutions. The modified algorithm obtains very good results on a set of large-scale continuous optimization benchmark problems. This is not the first time we see that the two aforementioned modifications make an initially non-competitive algorithm obtain state-of-the-art results. In previous work, we have shown that the same modifications substantially improve the performance of particle swarm optimization and ant colony optimization algorithms. Altogether, these results suggest that population growth coupled with local search help obtain high-quality results.
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Aydın, D., Liao, T., Montes de Oca, M.A., Stützle, T. (2012). Improving Performance via Population Growth and Local Search: The Case of the Artificial Bee Colony Algorithm. In: Hao, JK., Legrand, P., Collet, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2011. Lecture Notes in Computer Science, vol 7401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35533-2_8
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DOI: https://doi.org/10.1007/978-3-642-35533-2_8
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