Abstract
The standard Artificial Bee Colony (ABC) algorithm exhibits slow convergence speed and a tendency to get trapped in local optima under certain circumstances. To overcome these limitations, researchers have proposed a new ABC algorithm (GABC) by using a modified search strategy. During the process of searching for solutions, the GABC algorithm incorporates some randomly selected individuals and the global best individual. However, the GABC algorithm still has drawbacks such as low search accuracy and slow convergence speed. In response to these issues, an improved artificial bee colony algorithm (IABC) is proposed in this paper. The IABC algorithm introduces a dynamic inertia weight factor based on the GABC algorithm. A set of standard test functions are used to test the optimization of the improved artificial bee colony algorithm. Experimental results demonstrate that the proposed algorithm outperforms both the standard ABC algorithm and the GABC algorithm in terms of search accuracy and convergence speed.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant 62176273 and by National first-class undergraduate major in software engineering.
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Li, S., Zhang, W., Hao, J., Li, R., Chen, J. (2023). Artificial Bee Colony Algorithm Based on Improved Search Strategy. In: Yang, Y., Wang, X., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2023 . AIMS 2023. Lecture Notes in Computer Science, vol 14202. Springer, Cham. https://doi.org/10.1007/978-3-031-45140-9_1
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DOI: https://doi.org/10.1007/978-3-031-45140-9_1
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