| dc.date.accessioned | 2022-05-20T20:45:08Z | |
| dc.date.accessioned | 2022-10-19T00:41:21Z | |
| dc.date.available | 2022-05-20T20:45:08Z | |
| dc.date.available | 2022-10-19T00:41:21Z | |
| dc.date.created | 2022-05-20T20:45:08Z | |
| dc.date.issued | 2017 | |
| dc.date.issued | 2017 | |
| dc.identifier | http://hdl.handle.net/10533/253878 | |
| dc.identifier | 1160455 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4485030 | |
| dc.description.abstract | In this paper, we solve the Set Covering Problem with a
meta-optimization approach. One of the most popular models among
facility location models is the Set Covering Problem. The meta-level
metaheuristic operates on solutions representing the parameters of other
metaheuristic. This approach is applied to an Artificial Bee Colony metaheuristic
that solves the non-unicost set covering. The Artificial Bee
Colony algorithm is a recent swarm metaheuristic technique based on
the intelligent foraging behavior of honey bees. This metaheuristic owns
a parameter set with a great influence on the effectiveness of the search.
These parameters are fine-tuned by a Genetic Algorithm, which trains
the Artificial Bee Colony metaheuristic by using a portfolio of set covering
problems. The experimental results show the effectiveness of our
approach which produces very near optimal scores when solving set covering
instances from the OR-Library.
Keywords: Covering problems · Facility location · Artificial bee colony
algorithm · Swarm intelligence | |
| dc.language | eng | |
| dc.relation | Workshop on Engineering Applications, WEA | |
| dc.relation | instname: ANID | |
| dc.relation | reponame: Repositorio Digital RI2.0 | |
| dc.rights | http://creativecommons.org/licenses/by/3.0/cl/ | |
| dc.title | A meta-optimization approach for covering problems in facility location | |
