Abstract
A novel hybrid algorithm named GMBFO, with the combination between Grey Wolf Optimizer and the modified Bacterial Foraging Optimization, is presented in the paper. To improve the fixed chemotaxis step size in the standard BFO algorithm, the paper incorporates a nonlinear-decreasing adaptive mechanism into BFO. Besides that, an effective swarm learning strategy with the other three current global best individuals is proposed. In the dispersal and elimination step, we adopt the roulette wheel selection and local mutation mechanism to improve the diversity of the whole bacterial population. To testify the optimization performance of the proposed GMBFO, six benchmark functions with 45 dimensions are selected. Compared with BFO and the other three BFO variants, the GMBFO algorithm has an excellent capability in function optimization.
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Gan, X., Xiao, B. (2020). A Novel Hybrid Algorithm Based on Bacterial Foraging Optimization and Grey Wolf Optimizer. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_40
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DOI: https://doi.org/10.1007/978-3-030-60802-6_40
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