摘要： 提出一种基本粒子群算法(BPSO)改进方案,将基本粒子群算法粒子行为基于个体极值点和全局极值点变化为基于个体极值中心点和全局极值点,使得粒子能够获得更多的信息量来调整自身的状态。用3个基准函数对新算法进行了实验,结果表明,新算法在解的收敛性和稳定性等方面优于基本粒子群算法.

关键词: 粒子群, 优化, 进化计算, 群智能

Abstract: In this paper, A new vector named the average local best position is proposed to replace the local best of the traditional velocity update rule. In the new way, one particle can acquire more information of the others to adjust its movement. Three benchmark are tested and show that the new algorithm is better than the traditional particle swarm optimization with both a better value found and a steady convergence.

中图分类号: 

• TP301.6