Abstract
Inspired by branch and leaf growth behaviors of plants, a novel algorithm, named branch-leaf growth algorithm (BLGA), is presented for numerical optimization. In this algorithm, though branch and leaf implement different growth strategies, they cooperate closely to search the space for living resources. More specifically, branches grow into a stable self-similar architecture to support remote exploration, while leaves exploit local areas for better chances in each generation. An inhibition mechanism of plant hormones is applied to branches in case of overgrowth. In order to validate its efficiency, eight classic benchmark functions are adopted for test, and the results are compared with PSO, BFO and BCFO. The comparing results show that BLGA outperforms other evolutionary algorithms on most of benchmark functions.
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Acknowledgements
This research is partially supported by the open fund of Key Laboratory of Networked Control System, Chinese Academy of Sciences, and National Natural Science Foundation of China under Grants Nos. WLHKZ2014004, 61202341 and 61202495.
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He, X., Wang, J., Bi, Y. (2015). A Novel Branch-Leaf Growth Algorithm for Numerical Optimization. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_74
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