TY - GEN
T1 - A meta-optimization approach for covering problems in facility location
AU - Crawford, Broderick
AU - Soto, Ricardo
AU - Monfroy, Eric
AU - Astorga, Gino
AU - García, José
AU - Cortes, Enrique
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Artificial bee colony algorithm
KW - Covering problems
KW - Facility location
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=85030031213&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66963-2_50
DO - 10.1007/978-3-319-66963-2_50
M3 - Conference contribution
AN - SCOPUS:85030031213
SN - 9783319669625
T3 - Communications in Computer and Information Science
SP - 565
EP - 578
BT - Applied Computer Sciences in Engineering - 4th Workshop on Engineering Applications, WEA 2017, Proceedings
A2 - Figueroa-Garcia, Juan Carlos
A2 - Lopez-Santana, Eduyn Ramiro
A2 - Ferro-Escobar, Roberto
A2 - Villa-Ramirez, Jose Luis
PB - Springer Verlag
T2 - 4th Workshop on Engineering Applications, WEA 2017
Y2 - 27 September 2017 through 29 September 2017
ER -