Bacterial Foraging Oriented by Differential Evolution Strategy

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.