Paper Title:
An Improved Ant Colony Optimization Supervised by PSO
  Abstract

Combined with the idea of the particle swarm optimization (PSO) algorithm, the ant colony optimization (ACO) algorithm is presented to solve the well known traveling salesman problem (TSP). The core of this algorithm is using PSO to optimize the control parameters of ACO which consist of heuristic factor, pheromone evaporation coefficient and the threshold of stochastic selection, and applying ant colony system to routing. The new algorithm effectively overcomes the influence of control parameters of ACO and decreases the numbers of useless experiments, aiming to find the balance between exploiting the optimal solution and enlarging the search space.

  Info
Periodical
Advanced Materials Research (Volumes 108-111)
Edited by
Yanwen Wu
Pages
1354-1359
DOI
10.4028/www.scientific.net/AMR.108-111.1354
Citation
Z. G. Zhou, "An Improved Ant Colony Optimization Supervised by PSO", Advanced Materials Research, Vols. 108-111, pp. 1354-1359, 2010
Online since
May 2010
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