摘要
针对传统粒子群优化技术存在易陷入局部最优解而导致的收敛速度变慢、多样性差的缺点,对参数设置进行研究,并提出一种基于向量差的粒子群优化模型,将2个随机选择的不同向量差加权到粒子速度更新公式中,能够使粒子摆脱局部最小值而向全局搜索.经对几个典型函数的测试,表明该模型效果较好.
Aim at the shorting that traditional PSO easily trapped into local optimal solution which slows down the convergence and bad diversity.Setting the parameters of PSO is studied and proposed a PSO model based vector difference.The difference of two vectors is weighted in update formula of particle acceleration.It make particle break away from the local minimum and to search in global field.Tested several typical functions,it proved that the model has better affect.
出处
《高师理科学刊》
2010年第5期31-34,共4页
Journal of Science of Teachers＇College and University
关键词
粒子群优化
向量
优化算法
particle swarm optimization
vector
optimization algorithm
