Quantification-Based Ant Colony System for TSP

  • Ming ZhaoEmail author
  • Jeng-Shyang Pan
  • Chun-Wei Lin
  • Lijun Yan
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 238)


Ant Colony Optimization (ACO) is one of the swarm intelligent methods for solving computational problems, especially in finding the optimal paths through graphs. In the past, floating point is widely used to represent the pheromone in ACO, thus requiring large amounts of memory to find the optimal solutions. In this paper, the quantification-based ACS (QACS) is thus proposed to reduce the space complexity. New updating rules of pheromone with no decay parameters are also designed in the proposed QACS for simplifying the updating processing of pheromone. Based on the experimental results of proposed QACS, the convergence rate can be improved with less memory space for solving Traveling Salesman Problem (TSP).


Ant colony Optimization pheromone updating rule Qualificationbased TSP 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ming Zhao
    • 1
    Email author
  • Jeng-Shyang Pan
    • 1
  • Chun-Wei Lin
    • 1
  • Lijun Yan
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of Technology Shenzhen Graduate SchoolHarbinChina

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