@inproceedings{7e41233766a2465c89c8a949204dd987,
title = "An ant colony system based on the physarum network",
abstract = "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.",
keywords = "Ant Colony System, Physarum Network, TSP",
author = "Tao Qian and Zili Zhang and Chao Gao and Yuheng Wu and Yuxin Liu",
year = "2013",
doi = "10.1007/978-3-642-38703-6_35",
language = "英语",
isbn = "9783642387029",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "297--305",
booktitle = "Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings",
edition = "PART 1",
note = "4th International Conference on Advances in Swarm Intelligence, ICSI 2013 ; Conference date: 12-06-2012 Through 15-06-2012",
}