Abstract
This paper proposes an adaptive structure-redesigned-based bacterial foraging optimization called ASRBFO. In this improved algorithm, the chemotaxis step of SRBFO is adaptively adjusted based on the bacterial searching status. The personal current and best positions of bacteria as well as the mean of all bacterial positions are taken and used to calculate the chemotaxis step during the searching process. The goal of the study is to improve the convergence efficiency and the accuracy of SRBFO. To demonstrate the performance, six different benchmark functions are chosen to the experiment, and other three SRBFOs are used to compare with the proposed algorithm. The results show that ASRBFO outperforms other SRBFOs.
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Acknowledgment
This work is partially supported by The National Natural Science Foundation of China (Grants nos. 71571120, 71271140, 71461027, 71471158) and the Natural Science Foundation of Guangdong Province (Grant no. 2016A030310074).
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Tan, L.J., Yi, W.J., Yang, C., Feng, Y.Y. (2016). Adaptive Structure-Redesigned-Based Bacterial Foraging Optimization. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_80
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DOI: https://doi.org/10.1007/978-3-319-42294-7_80
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