The Physarum Network model exhibits the feature of important pipelines being reserved with the evolution of network during the process of solving a maze problem. Drawing on this feature, an Ant Colony System (ACS), denoted as PNACS, is proposed based on the Physarum Network (PN). When updating pheromone matrix, we should update both pheromone trails released by ants and the pheromones flowing in a network. This hybrid algorithm can overcome the low convergence rate and local optimal solution of ACS when solving the Traveling Salesman Problem (TSP). Some experiments in synthetic and benchmark networks show that the efficiency of PNACS is higher than that of ACS. More important, PNACS has strong robustness that is very useful for solving a higher dimension TSP.
This paper was presented at the International Conference on Advances in Swarm Intelligence (4th : 2013 : Harbin, China)
Field of Research
080108 Neural, Evolutionary and Fuzzy Computation 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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