• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Fakultet for informasjonsteknologi og elektroteknikk (IE)
  • Institutt for datateknologi og informatikk
  • View Item
  •   Home
  • Fakultet for informasjonsteknologi og elektroteknikk (IE)
  • Institutt for datateknologi og informatikk
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Incorporating Labor Division into Ant Colony Optimization

Grimnes, Jørgen Kjeldstad; Hovland, Jakob
Master thesis
Thumbnail
View/Open
15097_FULLTEXT.pdf (4.298Mb)
15097_COVER.pdf (1.556Mb)
URI
http://hdl.handle.net/11250/2407638
Date
2016
Metadata
Show full item record
Collections
  • Institutt for datateknologi og informatikk [3788]
Abstract
Ant colony optimization (ACO) is a constructivistic and population-based metaheuristic for solving combinatorial optimization problems inspired by how real ants use pheromones to find shortest paths. Like other metaheuristics ACO is prone to converge on local optima, also known as stagnation. Inspired by the collective behavior of real ants, this thesis incorporated labor division into ACO in order to avoid stagnation. Since there was little research that concern utilizing labor division in ACO, two original attempts at merging labor division models from biological science with ACO was performed. The two chosen labor division models were the seemingly popular Fixed-Threshold model and the Self-Reinforcement model.

The two resulting algorithms were applied to the minimum-cost flow problem domain with concave costs functions, which is a NP-hard problem. Max-Min Ant System, also an ACO algorithm, had previously been applied to concave minimum-cost flow problems and achieved good results. The performance and search behavior of the two proposed labor division algorithms were therefore compared to Max-Min Ant System on a set of 300 flow networks. The results indicated that both labor division algorithms performed comparable to Max-Min Ant System and avoided stagnation very well.
Publisher
NTNU

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit