Inspired by the dispersal mode of beans and the evolution of population distribution in nature, a novel bionic intelligent optimization algorithm-named bean optimization algorithm (BOA) is proposed. It has stable robust behavior on explored tests and stands out as a promising alternative to existing optimization methods. In this paper, an improved bean optimization algorithm, named chaotic bean optimization algorithm (CBOA), is introduced. The CBOA algorithm makes full use both of the fast convergence of the BOA algorithm and the ergodicity, stochastic, sensitivity properties of chaotic motions. The chaos sequence in CBOA is generated by using logic mapping function. The core contents of the algorithm include the several aspects: (1) Both of the diversity of individuals and the ergodicity of seeding locations in the initial are improved population by applying chaotic serialization for the initial bean group; (2) the distribution of offspring beans is optimized and the global optimization ability and stability of BOA are improved by producing tiny chaotic disturbance to offspring beans according to their father beans. In order to verify the validity of the CBOA, function optimization experiments are carried out, which include six typical benchmark functions and the CEC2014 benchmark functions. A comparative analysis is performed based on the experiments of particle swarm optimization and BOA. We also research on the characters of CBOA. A contrast analysis is carried out to verify the research conclusions about the relations between the algorithm parameters and its performance.