Journal of Systems Engineering and Electronics ›› 2010, Vol. 32 ›› Issue (11): 2453-2458.doi: 10.3969/j.issn.1001-506X.2010.11.42

• 软件、算法与仿真 • 上一篇    下一篇

多邻域改进粒子群算法

杨雪榕1,梁加红1,陈凌1,尹大伟2   

  1. 1. 国防科学技术大学机电工程与自动化学院, 湖南 长沙 410073;
    2. 国防科学技术大学航天与材料工程学院, 湖南 长沙 410073
  • 出版日期:2010-11-23 发布日期:2010-01-03

Multi-neighborhood improved particle swarm optimization algorithm

YANG Xue-rong1,LIANG Jia-hong1,CHEN Ling1,YIN Da-wei2   

  1. 1. Coll. of Mechanotronics Engineering and Automation, National Univ. of Defense Technology, Changsha 410073, China;
    2. School of Aerospace and Material Engineering, National Univ. of Defense Technology, Changsha 410073, China
  • Online:2010-11-23 Published:2010-01-03

摘要:

为了改进标准粒子群算法的性能,提出了多邻域改进粒子群算法。算法提出了一种较为简单的多邻域拓扑方案,对速度惯性权重的更新策略进行了改进,引入了速度和搜索区间限制算法。经过对经典测试函数的计算测试,算法表现出良好的复杂问题求解能力。最后,针对多目标优化问题,给出了多目标应用在粒子群算法中的处理方法,并对经典的5维优化和Golinski 减速器设计问题进行了求解,通过数据比对,证明了算法性能远优于现有的一些算法。

Abstract:

The multi-neighborhood improved particle swarm optimization algorithm (MNI-PSO) is proposed for the purpose of improving the capability of the standard particle swarm optimization (PSO). The MNI-PSO contains a simple neighborhood topology and sets an improved update scheme of the velocity inertial weight. The velocity and searching area restriction algorithms are also proposed for the MNI-PSO. The optimization results of the classical testing problems show that the MNI-PSO has performed a great capability for the complex optimization problems. Finally, the solution to multi-objective optimization problems (MOOP) using MNI-PSO is proposed. The classical 5-D optimization problem and Golinski’s speed reducer problem are optimized. The results show that the MNI-PSO’s performance is better than some other popular algorithms.