2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)

dc.creatorRojas-Morales, Nicolás
dc.creatorRiff-Rojas, María Cristina
dc.creatorNeveu, Bertrand
dc.date2021-08-23T22:57:24Z
dc.date2022-07-07T02:40:26Z
dc.date2021-08-23T22:57:24Z
dc.date2022-07-07T02:40:26Z
dc.date2018
dc.date.accessioned2023-08-23T00:29:42Z
dc.date.available2023-08-23T00:29:42Z
dc.identifier1151456
dc.identifier1151456
dc.identifierhttps://hdl.handle.net/10533/252044
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8355349
dc.descriptionA Constraint Satisfaction Problem is composed by a set of variables, their related domains and a set of constraints among the variables that must be satisfied. These are known as hard problems to be solved. Many algorithms have been proposed to solve these problems. Metaheuristics and in particular ant-based algorithms have been used to solve difficult instances. In this paper, we propose new heuristics to be included in an ant-based algorithm in order to improve its performance when tackling hard constraint satisfaction problems. These heuristics are focused on the availability of consistent variable values and to restrict the ants collaborative information to the feasibility. To evaluate these heuristics we used the well-known Ant Solver algorithm and tested with problem instances from the transition phase. Results show that using our heuristics the Ants algorithm increases the number of problems that it is able to solve. Finally, a statistical analysis is presented to compare these approaches.
dc.descriptionRegular 2015
dc.descriptionFONDECYT
dc.descriptionFONDECYT
dc.languageeng
dc.relationhandle/10533/111557
dc.relationhandle/10533/111541
dc.relationhandle/10533/108045
dc.relationhttps://doi.org/10.1109/CEC.2018.8477747
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.rightsinfo:eu-repo/semantics/article
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleFeasibility and Availability based Heuristics for ACO algorithms solving Binary CSP
dc.title2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
dc.typeArticulo
dc.typeinfo:eu-repo/semantics/publishedVersion


Este ítem pertenece a la siguiente institución