关键词: 量子粒子群, 最小二乘支持向量机, 基准测试, 参数优化

Abstract: Support Vector Machine（SVM） is a machine learning algorithm with good performance.The selection of its parameters has a great influence on its modeling accuracy and generalization performance，and it is an important area in machine learning research.Based on a brief introduction to basic Particle Swarm Optimization（PSO），a new quantum PSO algorithm is presented，as well as its implementation.Performance comparison with classic PSO algorithm is made through 4 benchmark test functions.Based on the proposed quantum PSO，the optimum parameter selection of Least Squares SVM（LS-SVM） is studied.Simulation results show that the presented quantum PSO algorithm can achieve good performance.

Key words: quantum Particle Swarm Optimization（PSO）, Least Squares Support Vector Machine（LS-SVM）, benchmark test, parameter optimization