Paper Info
Title
The effectiveness of dynamic ant colony tuning
Abstract
We examine the Genetically Modified Ant Colony System (GMACS) algorithm [3], which claims to dynamically tune an Ant Colony Optimization (ACO) algorithm to its near-optimal parameters. While our research indicates that the use of GMACS does result in higher quality solutions over a hand-tuned ACO algorithm, we found that the algorithm is ultimately hindered by its emphasis on randomized ant breeding. Specifically, our investigation shows that tuning ACO parameters on a single colony using a genetic algorithm, as done by GMACS, is not as effective as it may first appear and has several drawbacks.
Year
DOI
Venue
2007
10.1145/1276958.1276984
GECCO
Keywords
Field
DocType
ant colony optimization,genetically modified ant colony,tuning aco parameter,near-optimal parameter,higher quality solution,hand-tuned aco algorithm,randomized ant breeding,genetic algorithm,dynamic ant colony tuning,single colony,traveling salesman problem,genetically modified,ant colony,genetic algorithms
Ant colony optimization algorithms,Mathematical optimization,Computer science,Travelling salesman problem,Artificial intelligence,Ant colony,Genetic algorithm
Conference
Citations 
PageRank 
References 
0
0.34
5
Authors
2
Name
Order
Citations
PageRank
Adrian A. de Freitas1533.58
Christopher B. Mayer2254.46