Abstract
Bee colony optimization (BCO) is one of swarm intelligence algorithms that evolve static and locally. It performs slow improvement and tends to reach a local solution. In this paper, three modifications for the BCO are proposed, i.e. global evolution for some bees, dynamic parameters of the colony, and special treatment for the best bee. Computer simulation shows that Modified BCO performs quite better than the BCO for some job shop scheduling problems.
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Pertiwi, A.P., Suyanto (2011). Globally Evolved Dynamic Bee Colony Optimization. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23851-2_6
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DOI: https://doi.org/10.1007/978-3-642-23851-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23850-5
Online ISBN: 978-3-642-23851-2
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