Cooperative Approaches to Bacterial Foraging Algorithm for Clustering

Abstract

Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria, but it is difficult to optimize to get a high precision due to the randomness of the bacterial behavior, which belongs to intelligence algorithm. This paper presents an extended BFO algorithm, namely the Cooperative Bacterial Foraging Optimization (CBFO), which significantly improves the original BFO in solving clustering problems. A novel clustering method based on the CBFO could be used for solving clustering problems. In this work, firstly, The efficiency and performance of the CBFO algorithm was evaluated using six widely-used benchmark functions, coming up with comparative results produced by BFO, then Particle Swarm Optimization (PSO) is studied. Secondly, the algorithm with CBFO algorithms is used for data clustering on several benchmark data sets. The performance of the algorithm based on CBFO is compared with BFO algorithms on clustering problem. The simulation results show that the proposed CBFO outperforms the other three algorithms in terms of accuracy, robustness and convergence speed.