Adaptive Bacterial Foraging Optimization Based on Roulette Strategy

Abstract

Bacterial foraging optimization has drawn great attention and has been applied widely in various fields. However, BFO performs poorly in convergence when coping with more complex optimization problems, especially multimodal and high dimensional tasks. Aiming to address these issues, we therefore seek to propose a hybrid strategy to improve the BFO algorithm in each stage of the bacteria’s’ foraging behavior. Firstly, a non-linear descending strategy of step size is adopted in the process of flipping, where a larger step size is given to the particle at the very beginning of the iteration, promoting the rapid convergence of the algorithm while later on a smaller step size is given, helping enhance the particles’ global search ability. Secondly, an adaptive adjustment strategy of particle aggregation is introduced when calculating step size of the bacteria’s swimming behavior. In this way, the particles will adjust the step size according to the degree of crowding to achieve efficient swimming. Thirdly, a roulette strategy is applied to enable the excellent particles to enjoy higher replication probability in the replication step. A linear descent elimination strategy is adopted finally in the elimination process. The experimental results demonstrate that the improved algorithm performs well in both single-peak function and multi-peak function, having strong convergence ability and search ability.