An Improved Particle Swarm Optimization with Gaussian Disturbance

摘要：

　　The particle swarm optimization(PSO)is a widely used tool for solving optimization problems in the field of engineering technology.However,PSO is likely to fall into local optimum,which has the disadvantages of slow convergence speed and low convergence precision.In view of the above shortcomings,a particle swarm optimization with Gaussian disturbance is proposed.With introducing the Gaussian disturbance in the self-cognition part and social cognition part of the algorithm,this method can improve the convergence speed and precision of the algorithm,which can also improve the ability of the algorithm to escape the local optimal solution.The algorithm is simulated by Griewank function after the several evolutionary modes of GDPSO algorithm are analyzed.The experimental results show that the convergence speed and the optimization precision of the GDPSO is better than that of PSO.

作者: Changjun WEN Changlian LIU Heng ZHANG Hongliang WANG

作者单位: School of Mechanical Engineering,Hubei University of Technology,China

会议类型: 国际会议

会议名称: 2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)(2018第二届电子信息技术与计算机工程国际会议)(EITCE2018)

会议地点: 上海

会议语种:英文

页码: 1-4

在线出版日期: 2018-10-12（万方平台首次上网日期，不代表论文的发表时间）