dc.creator | Barba González, Cristóbal | es |
dc.creator | Ojalehto, Vesa | es |
dc.creator | García Nieto, José Manuel | es |
dc.creator | Nebro, Antonio J. | es |
dc.creator | Miettinen, Kaisa | es |
dc.creator | Aldana Montes, José F. | es |
dc.date.accessioned | 2021-05-05T09:20:54Z | |
dc.date.available | 2021-05-05T09:20:54Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Barba González, C., Ojalehto, V., García Nieto, J.M., Nebro, A.J., Miettinen, K. y Aldana Montes, J.F. (2018). Artificial Decision Maker Driven by PSO: An Approach for Testing Reference Point Based Interactive Methods. En PPSN 2018: 15th International Conference on Parallel Problem Solving from Nature (274-285), Coimbra, Portugal: Springer. | |
dc.identifier.isbn | 978-3-319-99252-5 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108530 | |
dc.description.abstract | Over the years, many interactive multiobjective optimization methods based on a reference point have been proposed. With a reference point, the decision maker indicates desirable objective function values to iteratively direct the solution process. However, when analyzing the performance of these methods, a critical issue is how to systematically involve decision makers. A recent approach to this problem is to replace a decision maker with an artificial one to be able to systematically evaluate and compare reference point based interactive methods in controlled experiments. In this study, a new artificial decision maker is proposed, which reuses the dynamics of particle swarm optimization for guiding the generation of consecutive reference points, hence, replacing the decision maker in preference articulation. We use the artificial decision maker to compare interactive methods. We demonstrate the artificial decision maker using the DTLZ benchmark problems with 3, 5 and 7 objectives to compare R-NSGA-II and WASF-GA as interactive methods. The experimental results show that the proposed artificial decision maker is useful and efficient. It offers an intuitive and flexible mechanism to capture the current context when testing interactive methods for decision making. | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades TIN2017-86049-R | es |
dc.description.sponsorship | Junta de Andalucía P12-TIC-1519 | es |
dc.format | application/pdf | es |
dc.format.extent | 12 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | PPSN 2018: 15th International Conference on Parallel Problem Solving from Nature (2018), pp. 274-285. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Multi-objective optimization | es |
dc.subject | Preference articulation | es |
dc.subject | Multiple criteria decision making | es |
dc.subject | Particle Swarm Optimization | es |
dc.title | Artificial Decision Maker Driven by PSO: An Approach for Testing Reference Point Based Interactive Methods | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | TIN2017-86049-R | es |
dc.relation.projectID | P12-TIC-1519 | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-319-99253-2_22 | es |
dc.identifier.doi | 10.1007/978-3-319-99253-2_22 | es |
dc.publication.initialPage | 274 | es |
dc.publication.endPage | 285 | es |
dc.eventtitle | PPSN 2018: 15th International Conference on Parallel Problem Solving from Nature | es |
dc.eventinstitution | Coimbra, Portugal | es |
dc.relation.publicationplace | Cham, Switzerland | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | Junta de Andalucía | es |