A Dynamic Inertia Weight Particle Swarm Optimization Algorithm Based on Gaussian Disturbance

摘要：

　　As one of the representatives of intelligent algorithm, Particle Swarm Optimization (PSO) has been widely concerned and applied since it was proposed. However, the traditional Particle Swarm Optimization (PSO) algorithm has some disadvantages, such as premature convergence, local optimization and lo resolution accuracy. In order to solve the problems in the algorithm, this paper proposes a dynamic inertia weight Particle Swarm Optimization algorithm based on Gaussian Disturbance. Through testing experiments with 5 benchmark functions, the improved algorithm has significantly improved its global search ability and optimization accuracy, and also overcomes the shortcoming of traditional Particle swarm Optimization (PSO).

关键词： Dynamic inertia weight Gaussian Disturbance Particle Swarm Optimization

作者: Fang Yiqiu Cheng Yuan Ge Junwei

作者单位: College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

会议类型: 国际会议

会议名称: 2018 3rd International Conference on Mechatronics and Information Technology (ICMIT 2018) (2018第三届机电与信息技术国际会议)

会议地点: 成都

会议语种:英文

页码: 1-6

在线出版日期: 2018-10-30（万方平台首次上网日期，不代表论文的发表时间）