Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (26): 46-48.DOI: 10.3778/j.issn.1002-8331.2009.26.013

• 研究、探讨 • Previous Articles     Next Articles

Social evolutionary programming algorithm for TSP

LAN Xiao-ling1,ZHOU Yong-quan2,WEI Xiu-xi1   

  1. 1.College of Computer and Electronic Information,Guangxi University,Nanning 530004,China
    2.College of Mathematics and Computer Science,Guangxi University for Nationalities,Nanning 530006,China
  • Received:2008-10-28 Revised:2008-12-22 Online:2009-09-11 Published:2009-09-11
  • Contact: LAN Xiao-ling



  1. 1.广西大学 计算机与电子信息学院,南宁 530004
    2.广西民族大学 数学与计算机科学学院,南宁 530006
  • 通讯作者: 蓝晓玲

Abstract: In this paper,a method-social evolutionary programming which combines social evolutionary programming and ant colony optimization is given to solve TSP.Firstly ant colony optimization is used as cognitive agents’ cognitive learning,and then the global optimum is obtained by paradigm’s learning and shift.Finally two examples are compared with the optimum that has been known,the result indicates that social evolutionary programming with fewer agents and less iterative times can also converge the optimum.

Key words: social evolutionary programming, ant colony optimization, Travelling Salesman Problem(TSP)

摘要: 将社会演化算法和蚁群算法相结合,以蚁群算法作为认知主体的推理过程,再以范式的学习和更新方式获得最优解,提出一种求解TSP问题的社会演化算法。最后通过两个算例实验仿真与TSP已知最优解进行对比分析,结果表明,社会演化算法在种群规模较小,迭代次数较少的情况下也可获得TSP最优解。

关键词: 社会演化算法, 蚁群算法, 旅行商问题

CLC Number: