An Improved Bacterial Foraging Optimization Algorithm

Aiming at the shortcomings in bacterial foraging optimization algorithm, e. g. , slower convergence speed, lower precision, easily falling into the local optimal solution, this paper proposes an improved bacterial foraging optimization algorithm. The fixed step adjusting is replaced by an adaptive step adjusting strategy via decreasing nonlinearly. By means of the idea of artificial bee colony, a mixed updating method on the bacterial position is introduced. The fittest selection criterion is improved and the crossover operator is introduced into the reproduced parents while retaining the best individual. The population evolution factor is proposed in order to prevent the stop of the evolution. Finally, the proposed algorithm is tested on the classic functions and the tuning of PID parameters, which shows their effectiveness.