A Novel Adaptive Chaotic Bacterial Foraging Optimization Algorithm

Abstract

In this paper, an adaptive chaotic bacterial foraging optimization (ACBFO) is presented. The improved bacterial foraging algorithm contains two new features, adaptive chemotaxis step setting and chaotic perturbation operation in each chemotactic event. The adaptive chemotaxis step setting enables fast convergence speed and good convergence accuracy of the algorithm, and the bacteria chaotic perturbation operation further allows the search to escape from local optima and achieve better convergence accuracy. With five benchmark functions, ACBFO is proved to have a better performance than the original bacterial foraging optimization (BFO) and BFO with linear deceasing chemotaxis step (BFO-LDC).