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A New Optimization Algorithm Based on Ant Colony System with Density Control Strategy

  • Ling Qin
  • Yixin Chen
  • Ling Chen
  • Yuan Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

A new optimization algorithm based on the ant colony system is presented by adopting the density control strategy to guarantee the performance of the algorithm. In each iteration of the algorithm, the solutions are selected to have mutation operations according to the quality and distribution of the solution. Experimental results on the traveling salesman problem show that our algorithm can not only get diversified solutions and higher convergence speed than the Neural Network Model and traditional ant colony algorithm, but also avoid the stagnation and premature problem.

Keywords

Neural Network Model Travel Salesman Problem Travel Salesman Problem Mutation Operation Density Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ling Qin
    • 1
    • 3
  • Yixin Chen
    • 2
  • Ling Chen
    • 1
    • 3
  • Yuan Yao
    • 1
  1. 1.Department of Computer ScienceNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Department of Computer Science and EngineeringWashington University in St. LouisSt. LouisUSA
  3. 3.Department of Computer ScienceYangzhou UniversityYangzhouChina

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