Abstract
Because of the powerful searching ability of artificial bee colony algorithm, it has applications in various fields. However, it still has a drawback on local search ability. Therefore, an adaptive selection probability ABC algorithm (called PABC) is proposed to improve its local search ability. In the multi-strategy search solutions, a probability is assigned to each strategy and the probability is adaptive adjusted to control the choice of strategy. Meanwhile, a modified mean center is introduced to replace the global best solution to guide search. The proposed PABC is proved to have better optimization ability than some other improved ABCs by testing classical 12 functions.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 61663028).
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Xiao, S., Wang, H., Xu, M., Wang, W. (2020). Artificial Bee Colony Based on Adaptive Selection Probability. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_2
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DOI: https://doi.org/10.1007/978-981-15-5577-0_2
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