Abstract
This paper presents an Improved Bacterial Foraging Optimization (IBFO) to solve the high computational complexity and less conductive search capability of the original BFO. A single loop implementation structure is adopted to reduce the computational complexity of the original algorithm with a triple-nested implementation structure. We adopt a cuckoo search in chemotaxis operation to increase the randomness of step size and improve search efficiency. Additionally, a new reproduction strategy is explored by employing the Lévy flight strategy to generate new individuals to replace the less conductive ones evaluated and sorted according to the current fitness values rather than accumulated fitness cost. Finally, the candidate mechanism is introduced into reproduction and elimination-dispersal events for comparing and obtaining a better solution. The proposed algorithm's effectiveness is compared with 6 well-known heuristic algorithms on 12 benchmark functions. The results indicate that the proposed IBFO outperforms other algorithms significantly in most cases.
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Acknowledgments
This work is partially supported by The National Natural Science Foundation of China (Grants Nos. 71901152), Natural Science Foundation of Guangdong Province (2020A1515010752), Natural Science Foundation of Shenzhen University (85303/00000155), and Scientific Research Team Project of Shenzhen Institute of Information Technology (SZIIT2019KJ022), Guangdong Basic and Applied Basic Research Foundation (Project No. 2019A1515011392).
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Xing, T., Wan, M., Wen, S., Chen, L., Wang, H. (2021). An Improved Bacterial Foraging Optimization for Global Optimization. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_30
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DOI: https://doi.org/10.1007/978-981-16-7502-7_30
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