A Bacterial Foraging Algorithm with Adaptive Step Length According to Adaptability Changes

Bionic algorithm is an important theoretical basis and indispensable method of modern science, which is widely used in various fields. Among them, bacterial foraging algorithm(BFA) is more flexible than other evolutionary algorithms, adapts to solve many practical problems, and has strong adaptability and robustness. However, BFA is more complex and the calculation amount is large. From the point of view of the natural selection behavior of bacteria tending to be suitable for the environment and avoid harm, this paper puts forward a improved BFA that realizes adaptive step according to the change of environmental adaptability value, and uses the initial setting step as a relative reference value to dynamically adjust the step forward of each bacteria. Using the Resenbrock function as the evaluation function, the test results show that the improved BFA converges faster, solves with higher precision and has better stability.