计算机应用 ›› 2011, Vol. 31 ›› Issue (11): 3094-3096.DOI: 10.3724/SP.J.1087.2011.03094

• 人工智能 • 上一篇    下一篇

基于带收缩因子的粒子群优化算法的二重数值积分

施美珍,林健良   

  1. 华南理工大学 理学院,广州 510640
  • 收稿日期:2011-05-05 修回日期:2011-06-20 发布日期:2011-11-16 出版日期:2011-11-01
  • 通讯作者: 施美珍
  • 作者简介:施美珍(1986-),女,江西南昌人,硕士研究生,主要研究方向:概率论、数理统计;
    林健良(1956-),男,广东佛山人,副教授,主要研究方向:运筹、数理统计。

Two dimensional numerical integration based on particle swarm optimization with constriction factor algorithm

SHI Mei-zhen,LIN Jian-liang   

  1. School of Science, South China University of Technology, Guangzhou Guangdong 510640, China
  • Received:2011-05-05 Revised:2011-06-20 Online:2011-11-16 Published:2011-11-01
  • Contact: SHI Mei-zhen

摘要: 提出了基于带收缩因子的粒子群优化(PSO-CF)算法求解二重数值积分的方法。PSO-CF算法初始时在积分区域内随机选取一定的分割点,粒子的速度采用收缩因子进行更新,粒子将朝着更好的位置移动。该算法基于分割后的每一个小矩形的4顶点和4内点及中心点定义适应值,用来评价粒子的优劣,通过反复迭代优化粒子。PSO-CF算法对最优粒子采用复化4内点公式计算二重数值积分。仿真实例表明,该算法积分精度较高,效果良好。

关键词: 二重数值积分, 优化, 粒子群优化算法, 收缩因子, 代数精度

Abstract: A new method was presented to calculate two dimentional numerical integration based on Particle Swarm Optimization (PSO) with a constriction factor, named PSO-CF. PSO-CF algorithm selected some initial partition points randomly in the domain,and used the constriction factor to update the velocity,then the particles would move towards better position.This algorithm evaluated particles by fitness value based on the four vertices, four interior points and the center point of every small rectangle after partition, it optimized the particles through repeated iteration. For the opitimal particle, PSO-CF algorithm used the composite four interior points to calculate two dimentional numerical integration. The experimental results show that integral precision is higher,and this method is effective.

Key words: two dimensional numerical integration, optimization, Particle Swarm Optimization (PSO) algorithm, constriction factor, algebraic accuracy