Support Vector Regression（SVR） is a machine learning method based on statistical learning theory, and is mainly used to deal with regression problems. Selecting appropriate parameters is a prerequisite for realizing the advantages of the SVR algorithm, but in practice there is still a difficulty in selecting model parameters. The swarm intelligence algorithm is a meta-heuristic algorithm that imitates the social behavior of biological populations in the natural world. It has the characteristics of simplicity, adaptability and flexibility, and has become a research hotspot of nonlinear parameter optimization methods. The research progress using swarm intelligence algorithm to optimize the parameters of SVR is comprehensively reviewed. Specifically, after introducing the basic theory of SVR, it systematically analyzes the advantages and shortcomings of common swarm intelligence algorithms and their improved methods for realizing the selection and optimization of SVR parameters, and finally gives prospects for future research directions and challenges.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2104-0096