To solve problems such as easily getting trapped in local optimum and slow convergence rate in BareBones Particle Swarm Optimization (BBPSO) algorithm, a dynamic Multi-Subgroup collaboration Barebones Particle Swarm Optimization based on Kernel Fuzzy Clustering (KFC-MSBPSO) was proposed. Based on the standard BBPSO algorithm, firstly, kernel fuzzy clustering method was used to divide the main group into several subgroups, and the subgroups optimized collaboratively to improve the searching efficiency. Then, nonlinear dynamic mutation factor was introduced to control subgroup mutation probabilities according to the number of particles and convergence conditions, the main group was reconstructed by means of particle mutation and the exploration ability was improved. The main group particle absorption strategy and subgroup merge strategy were proposed to strengthen the information exchange between main group and subgroups and enhanced the stability of the algorithm. Finally, the subgroup reconstruction strategy was used to adjust the iterations of subgroup reconstruction by combining the optimal solutions. The results of experiments on six benchmark functions, such as Sphere, show that the accuracy of KFC-MSBPSO algorithm has improved by at least 11.1% compared with classical BBPSO algorithm, Opposition-Based Barebones Particle Swarm Optimization (OBBPSO) algorithm and other improved algorithms. The best mean value in high dimensional space accounts for 83.33% and has a faster convergence rate. This indicates that KFC-MSBPSO algorithm has good search performance and robustness, which can be applied to the optimization of high dimensional complex functions.