Abstract
This paper proposes a variant of the bacterial foraging optimization (BFO) algorithm with time-varying chemotaxis step length and comprehensive learning strategy, namely Adaptive Comprehensive Learning Bacterial Foraging Optimization (ALCBFO). An adaptive non-linearly decreasing modulation model is used to balance the exploration and exploitation. The comprehensive learning mechanism is adopted to maintain the diversity of the bacterial population and thus alleviates the premature convergence. Compared with the classical GA, PSO, the original BFO and two improved BFOs (BFO-LDC and BFO-NDC), the proposed ACLBFO shows significantly better performance in solving multimodal problems.
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Tan, L., Wang, H., Liang, X., Xing, K. (2013). An Adaptive Comprehensive Learning Bacterial Foraging Optimization for Function Optimization. In: Huang, DS., Gupta, P., Wang, L., Gromiha, M. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2013. Communications in Computer and Information Science, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39678-6_33
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DOI: https://doi.org/10.1007/978-3-642-39678-6_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39677-9
Online ISBN: 978-3-642-39678-6
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