Abstract
In order to overcome the defects of slow convergence speed and low precision appeared in the original artificial bee colony (ABC) algorithm, a novel and improved adaptive ABC algorithm is presented in this paper. By dynamically adapting the step length that controls the range of neighborhood during the process of search, the proposed algorithm produces three candidate solutions that have good performances in exploiting in small search space, exploring in large search space and remaining initial search space, respectively. For illustration, a single variable function is utilized to analyze the cause of low precision and slow convergence speed. In addition, a different probability selection strategy is introduced to maintain population diversity of the bee colony. The improved ABC algorithm is tested on five numerical optimization functions and compared with the original ABC algorithm and a novel ABC algorithm known as ABC-SAM. The results show that the improved ABC algorithm is superior to two other algorithms on convergence and optimization precision.
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Acknowledgments
This study is funded by the Ministry of Education Returned Scientific Research Foundation.
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He, L., Bai, Q. (2014). An Improved Adaptive Artificial Bee Colony Algorithm. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_44
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DOI: https://doi.org/10.1007/978-3-642-54924-3_44
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