Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (25): 69-71.

• 学术探讨 • Previous Articles     Next Articles

New intelligent ACO for TSP

GU Jun-hua,TAN Qing,LI Na-na,MAO Ning   

  1. School of Computer Science and Engineering,Hebei University of Technology,Tianjin 300130,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2007-09-01 Published:2007-09-01
  • Contact: GU Jun-hua

一种新的求解TSP问题智能蚁群优化算法

顾军华,谭 庆,李娜娜,毛 宁   

  1. 河北工业大学 计算机科学与软件学院,天津 300130
  • 通讯作者: 顾军华

Abstract: A new intelligent Ant Colony Optimization(ACO) for Traveling Salesman Problem(TSP) has been proposed.The new algorithm extracts the intrinsic characteristic rule of TSP and then injects it into the elite of ants,which improves the elite ant’s capability to build a better solution and then makes an improvement of ACO.This paper not only describes the new algorithm’s theory,but also makes a simulation.The simulation results show that proposed algorithm finds optimum solutions more effectively both in time and quantity than ACO.

Key words: Ant Colony Optimization, intelligence, Traveling Salesman Problem, cross

摘要: 提出了一种新的用于求解TSP问题的智能蚁群优化算法。新算法从TSP问题本身出发,提取出了该问题的一种本质特征,并赋予蚁群算法中的精英蚂蚁以识别该固有特征的能力,以提高精英蚂蚁的搜索质量,进而使得新算法整体的求解能力得以提高。文章中不仅阐述了新算法的原理,而且进行了仿真实验,实验结果表明新算法在求解时间和求解质量上都取得了很好的效果。

关键词: 蚁群优化算法, 智能, 旅行商问题, 交叉