Abstract
To improve optimizing performance of artificial bee colony (ABC), a new algorithm called learnable artificial bee colony (LABC) is presented in this paper. The new algorithm employs some available knowledge from the two optimization phases to guide the next optimization process. Eight benchmark functions are used to validate its optimization effect. The experimental results show that LABC outperforms ABC and particle swarm optimization (PSO) on most benchmark functions. LABC provides a new reference for improving optimization performance of ABC.
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Acknowledgment
The authors are very grateful to the anonymous reviewers for their valuable suggestions and comments to improve the quality of this paper. This research is partially supported by National Science and Technology Support Program of China 2012BAF10B06, supported by National Science and Technology Support Program of China 2012BAF10B11, supported by National Natural Science Foundation of China 61174164, supported by National Natural Science Foundation of China 61105067 and supported by National Natural Science Foundation of China 51205389.
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Qi, X., Zhu, Y., Nan, L., Ma, L. (2013). A Learnable Artificial Bee Colony Algorithm for Numerical Function Optimization. In: Qi, E., Shen, J., Dou, R. (eds) Proceedings of 20th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40063-6_35
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DOI: https://doi.org/10.1007/978-3-642-40063-6_35
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