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A butterfly optimization algorithm (BOA) based on chaotic feedback sharing and group synergy (CFSBOA) is proposed to solve the shortcomings of low precision and easy to fall into local optimum. Firstly, using Hénon chaos to initialize the population can make the population cover the search blind area as much as possible, increase the diversity of the population, and improve the ability of optimizing the algorithm. Secondly, using the ideas of positive and negative feedback mechanism in feedback control circuit, it builds butterfly feedback shared communication network, allowing individuals to receive information from multiple directions to help populations of positioning the location of the optimal solution and perform careful search, enhance the ability to escape from local optimum and accelerate the algorithm convergence speed. Finally, the collective synergistic effect mechanism is used to improve and balance the global and local search ability and enhance the global and local optimization ability of the algorithm. The performance of the improved butterfly optimization algorithm is verified by using different dimension benchmark test functions, statistical test, Wilcoxon test and multiple types of CEC2014 partial functions. Compared with the new improved butterfly algorithm and other swarm intelligence algorithms, the experimental results show that the proposed algorithm has obvious advantages.