Abstract
Bacterial foraging optimization (BFO) algorithm is one of the newest nature inspired optimization algorithm, based on social foraging behavior of Escherichia coli. However, this swarm-based algorithm is computationally expensive due to the slow nature of the collective intelligence of bacterial swarm. This paper presents a novel way to accelerate BFO. The novel bacterial foraging oriented by differential evolution strategy(BFODE) adds differential evolution operators to the bacterial swarm to have a better tumble movements in chemotaxis steps of virtual bacterial. A comprehensive set of complex benchmark functions including a wide range of dimensions is employed for experimental verification. Experimental results confirm that the BFODE outperforms the original BFO in terms of convergence speed and solution accuracy.
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