Open Access
Issue
MATEC Web Conf.
Volume 246, 2018
2018 International Symposium on Water System Operations (ISWSO 2018)
Article Number 03015
Number of page(s) 6
Section Parallel Session II: Water System Technology
DOI https://doi.org/10.1051/matecconf/201824603015
Published online 07 December 2018
  1. Xu Zongben. bionics in computational intelligence [M]. science press, 2003. [Google Scholar]
  2. Stützle T. Ant Colony Optimization[J]. Computational Intelligence Magazine IEEE, 2007, 1 (4): 28-39. [Google Scholar]
  3. Colorni A Dorigo M, Maniezzo V. Distributed optimization by ant colonies. In: Varela F, Bourgine P, eds. Proc. of the ECAL’91 European Conf of Artificial Life. Paris: Elsevier. 1991. 134-144. [Google Scholar]
  4. Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 1996, 26 (1): 29-41. [CrossRef] [Google Scholar]
  5. Zhao Yuxin. new meta-heuristic optimization method [M]. science press, 2013. [Google Scholar]
  6. Gómezcabrero D, Ranasinghe D N. Fine-tuning the Ant Colony System algorithm through Particle Swarm Optimization[J]. 2018, 25 (25): 73-7. [Google Scholar]
  7. Lissovoi A, Witt C. MMAS Versus Population-Based EA on a Family of Dynamic Fitness Functions[J]. Algorithmica, 2016, 75 (3): 554-576. [CrossRef] [Google Scholar]
  8. Yuan Wang Huang, you Xiao Ming, Liu sheng, et al. adaptive simulated annealing ant colony algorithm for tsp [J]. computer applications and software, 2018 (2): 261-266. [Google Scholar]
  9. Liu G, He D. An improved Ant Colony Algorithm based on dynamic weight of pheromone updating[C]//Ninth International Conference on Natural Computation. IEEE, 2014: 496-500. [Google Scholar]
  10. Wei X M. Hybrid Behavior Ant Colony Algorithm[J]. Advanced Materials Research, 2012, 433-440: 4496-4499. [Google Scholar]
  11. Gao R, Wu J. Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization[J]. Future Internet, 2015, 7 (4): 465-483. [CrossRef] [Google Scholar]
  12. Xu J M, Cao X B, Wang X F. Polymorphic Ant Colony Algorithm[J]. Journal of University of Science & Technology of China, 2005 (1): 59-65. [Google Scholar]
  13. Jie L I, Zhen-Bo L I, Chen J P. A Hybrid Algorithm Based on Genetic Algorithm and Ant Colony Algorithm for Wireless Network Location[J]. Computer Engineering & Software, 2017. [Google Scholar]
  14. Chen W, Jiang Y. Improving ant colony algorithm and particle swarm algorithm to solve TSP problem[J]. Information Technology, 2016. [Google Scholar]
  15. Liu F, Zhong J, Liu C, et al. A novel strategy of initializing the population size for ant colony optimization algorithms in TSP[C]//International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. 2017: 249-253. [Google Scholar]
  16. Jiang J, Gao J, Li G, et al. Hierarchical Solving Method for Large Scale TSP Problems[M]//Advances in Neural Networks - ISNN 2014. Springer International Publishing, 2014: 252-261. [Google Scholar]
  17. Şaban Gülcü, Mahi M, Ömer Kaan Baykan, et al. A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem[J]. Soft Computing, 2016 (2): 1-17. [Google Scholar]
  18. Marvin frost, Zhang Hongwei. tsp based on improved ACS - 3 - opt ant colony algorithm [J]. computer engineering, 2008, 34 (19): 200–202. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.