Improved Bacterial Foraging Optimization Algorithm Based on Fuzzy Control Rule Base

Manual construction of a rule base for a fuzzy system is the hard and time-consuming task that requires expert knowledge. In this paper we proposed a method based on improved bacterial foraging optimization (IBFO), which simulates the foraging behavior of E.coli bacterium, to tune the Gaussian membership functions parameters of an improved Takagi-Sugeno-Kang fuzzy system (C-ITSKFS) rule base. To remove the defect of the low rate of convergence and prematurity, three modifications were produced to the standard bacterial foraging optimization (BFO). As for the low accuracy of finding out all optimal solutions with multi-method functions, the IBFO was performed. In order to demonstrate the performance of the proposed IBFO, multiple comparisons were made among the BFO, particle swarm optimization (PSO), and IBFO by MATLAB simulation. The simulation results show that the IBFO has a superior performance.