Aiming at the shortcomings such as low optimization precision

low convergence rate and easy to fall into local optimum for bacteria foraging algorithm

an improved optimization algorithm of bacteria foraging is proposed. The improved particle swarm algorithm is applied to chemotaxis of bacteria foraging algorithm to change size of step and the direction of the flip

to speed up the exchange of information between individuals to enhance the convergence rate and precision of the foraging algorithm. Use the fitness value to choose the migration mode

the migration mode includes direct migration

probabilistic migration and not migratory

and the probability of individual migration is dynamically adjusted according to the fitness value to reduce the misplaced or stopped situation. The simulation experiments are carried out with six standard functions and proportion integration differentiation（ PID） parameters. Compare the results of the algorithm of bacterial foraging and the improved bacterial foraging algorithm to verify the effectiveness and practicability of the improved algorithm.