AntNet with reward-penalty reinforcement learning

Pooia Lalbakhsh*, Bahram Zaeri, Ali Lalbakhsh, Mehdi N. Fesharaki

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

11 Citations (Scopus)

Abstract

The paper deals with a modification in the learning phase of AntNet routing algorithm, which improves the system adaptability in the presence of undesirable events. Unlike most of the ACO algorithms which consider reward-inaction reinforcement learning, the proposed strategy considers both reward and penalty onto the action probabilities. As simulation results show, considering penalty in AntNet routing algorithm increases the exploration towards other possible and sometimes much optimal selections, which leads to a more adaptive strategy. The proposed algorithm also uses a self-monitoring solution called Occurrence-Detection, to sense traffic fluctuations and make decision about the level of undesirability of the current status. The proposed algorithm makes use of the two mentioned strategies to prepare a self-healing version of AntNet routing algorithm to face undesirable and unpredictable traffic conditions.

Original languageEnglish
Title of host publicationProceedings - 2nd International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2010
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages17-21
Number of pages5
ISBN (Print)9780769541587
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2nd International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2010 - Liverpool, United Kingdom
Duration: 28 Jul 201030 Jul 2010

Other

Other2nd International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2010
CountryUnited Kingdom
CityLiverpool
Period28/07/1030/07/10

Keywords

  • Ant colony optimization
  • AntNet
  • Reward-penalty reinforcement learning
  • Swarm intelligence

Fingerprint Dive into the research topics of 'AntNet with reward-penalty reinforcement learning'. Together they form a unique fingerprint.

Cite this