Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (8): 59-64.doi: 10.16180/j.cnki.issn1007-7820.2020.08.010

Previous Articles     Next Articles

Ant Colony Optimization for Dynamic Pheromone Update Strategy Based on Congestion Factor

ZHU Hongwei,ZHANG Hainan   

  1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China
  • Received:2019-05-20 Online:2020-08-15 Published:2020-08-24
  • Supported by:
    National Natural Science Foundation of China(61673258);National Natural Science Foundation of China(61075115);National Natural Science Foundation of China(61403249);National Natural Science Foundation of China(61603242)


Aiming at the problem that the ant colony algorithm is easy to fall into local optimum and the convergence speed is slow, this paper proposed an ant colony optimization based on CFACS. The idea of crowding degree in the Fish Swarm algorithm was introduced to expand the distribution range of ants in the population, which made the ants explored more solution space and improve the global search ability of the algorithm. The dynamic pheromone update strategy was adopted to adaptively adjust the pheromone concentration released by the current optimal path in each iteration to ensure the diversity of the ant colony in the early stage and to ensure the convergence of the algorithm in the later stage. The simulation experiments for solving the TSP problem showed that the improved algorithm could obtain the solution quality and the convergence speed of the solution were better than the traditional ant colony algorithm, which balanced the contradiction between population diversity and convergence speed.

Key words: congestion factor, dynamic pheromone update strategy, traveling salesman problem, ant colony optimization, fish swarm algorithm, convergence

CLC Number: 

  • TP18