AntNet: Distributed stigmergetic control for communications networks. This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet’s agents, concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms’ performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.

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  1. Vallikannu, R.; George, A.; Srivatsa, S. K.: Autonomous localization based energy saving mechanism in indoor MANETs using ACO (2015)
  2. Giagkos, Alexandros; Wilson, Myra S.: BeeIP -- a swarm intelligence based routing for wireless ad hoc networks (2014) ioport
  3. Marinakis, Yannis; Migdalas, Athanasios: A particle swarm optimization algorithm for the multicast routing problem (2014)
  4. Pintea, Camelia-Mihaela: Advances in bio-inspired computing for combinatorial optimization problems (2014)
  5. Sattari, Mohammad Reza Jabbarpour; Malakooti, Hossein; Jalooli, Ali; Noor, Rafidah Md: A dynamic vehicular traffic control using ant colony and traffic light optimization (2014)
  6. Hu, Xiao-Min; Zhang, Jun: Minimum cost multicast routing using ant colony optimization algorithm (2013)
  7. Di Stefano, Antonella; Morana, Giovanni: A bio-inspired distributed algorithm to improve scheduling performance of multi-broker grids (2012)
  8. Gorodetskii, V. I.: Self-organization and multiagent systems. I: Models of multiagent self-organization (2012)
  9. Saleem, Muhammad; Ullah, Israr; Farooq, Muddassar: \textitBeeSensor: an energy-efficient and scalable routing protocol for wireless sensor networks (2012) ioport
  10. Sudholt, Dirk; Thyssen, Christian: Running time analysis of ant colony optimization for shortest path problems (2012)
  11. Benešová, Barbora: Global optimization numerical strategies for rate-independent processes (2011)
  12. Di Caro, Gianni; Dorigo, Marco: Antnet: distributed stigmergetic control for communications networks (2011) ioport
  13. Martens, David; Baesens, Bart; Fawcett, Tom: Editorial survey: swarm intelligence for data mining (2011) ioport
  14. Saleem, Muhammad; Di Caro, Gianni A.; Farooq, Muddassar: Swarm intelligence based routing protocol for wireless sensor networks: survey and future directions (2011) ioport
  15. Khoukhi, Lyes; Cherkaoui, Soumaya: Intelligent QoS management for multimedia services support in wireless mobile ad hoc networks (2010)
  16. Meisel, Michael; Pappas, Vasileios; Zhang, Lixia: A taxonomy of biologically inspired research in computer networking (2010)
  17. Paquereau, Laurent; Helvik, Bjarne E.: Ensuring fast adaptation in an ant-based path management system (2010)
  18. Vrancx, Peter; Verbeeck, Katja; Nowé, Ann: Analyzing the dynamics of stigmergetic interactions through pheromone games (2010)
  19. Wang, Hua; Meng, Xiangxu; Li, Shuai; Xu, Hong: A tree-based particle swarm optimization for multicast routing (2010)
  20. Yeom, Kiwon: Bio-inspired self-organization for supporting dynamic reconfiguration of modular agents (2010) ioport

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