J4 ›› 2010, Vol. 07 ›› Issue (4): 636-640.

• 计算机科学 • 上一篇    下一篇

用IAA求解约束模型多解问题

袁华1,2, 李文辉2, 常欣1,2   

  1. 1. 长春工业大学 计算机科学与工程学院, 长春 130012|2. 吉林大学 计算机科学与技术学院, 长春 130012
  • 收稿日期:2009-09-24 出版日期:2010-07-26 发布日期:2011-06-14
  • 通讯作者: 李文辉 E-mail:liwenhui2050@163.com

IAA for Constraint Model Multisolution Problem

YUAN Hua1,2, LI Wenhui2, CHANG Xin1,2   

  1. 1. School of Computer Science &|Engineering, Changchun University of Technology, Changchun 130012, China;2. College of Computer Science and Technology, Jilin University, Changchun 130012, China
  • Received:2009-09-24 Online:2010-07-26 Published:2011-06-14
  • Contact: LI Wenhui E-mail:liwenhui2050@163.com

摘要:

针对蚁群优化算法易于陷入早熟收敛和局部求精能力不足的缺点, 提出一种用免疫蚁群算法(IAA)寻找最优解的方法. 算法基于人工免疫系统原理, 设计了具有免疫能力的蚂蚁抗体保持蚁群的多样性, 在迭代后期蚁群依然保持进化能力, 提高了算法的局部求精能力, 使蚁群优化算法在局部开采与全局探索间都取得了更好的平衡. 实验结果表明, 算法具有良好的优化性能和时间性能.

关键词: 几何约束模型; 蚂蚁算法; 免疫算法

Abstract:

Immune Ant Algorithm(IAA) is proposed for preventing ACO from premature convergence and improving the precision of local optimization algorithm to develop ant operators with immunity based on the basic principles of artificial immune systems. The immune ant operators will create better balance between exploration and exploitation by keeping the diversity of ant colony, maintaining the intensification in the later iteration phase, and improving the precision of local optimization algorithm. The algorithm has both good optimization capability and time capability.

Key words: geometric constraint model, ant algorithm, immune algorithm

中图分类号: 

  • TP31