A4 Artikkeli konferenssijulkaisussa
Interactive MOEA/D with multiple types of preference information (2022)
Lárraga, G., & Miettinen, K. (2022). Interactive MOEA/D with multiple types of preference information. In J. E. Fieldsend (Ed.), GECCO '22 : Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1826-1834). ACM. https://doi.org/10.1145/3520304.3534013
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Lárraga, Giomara; Miettinen, Kaisa
Emojulkaisu: GECCO '22 : Proceedings of the Genetic and Evolutionary Computation Conference Companion
Emojulkaisun toimittajat: Fieldsend, Jonathan E.
Konferenssin paikka ja aika: Boston, Massachusetts, USA, 9.-13.7.2022
ISBN: 978-1-4503-9268-6
Julkaisuvuosi: 2022
Ilmestymispäivä: 19.07.2022
Artikkelin sivunumerot: 1826-1834
Kirjan kokonaissivumäärä: 2345
Kustantaja: ACM
Julkaisumaa: Yhdysvallat (USA)
Julkaisun kieli: englanti
DOI: https://doi.org/10.1145/3520304.3534013
Julkaisun avoin saatavuus: Avoimesti saatavilla
Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava
Tiivistelmä
Multiobjective optimization has the ultimate aim of helping a decision-maker to find a satisfying solution for a problem with multiple conflicting objectives. Usually, multiobjective evolutionary algorithms have been utilized to approximate the complete Pareto optimal set. However, some such algorithms incorporate preference information to direct the search toward a region of interest. Interactive methods allow decision-makers to learn more about the problem and update their preference information iteratively. Although preference information can be represented in multiple ways, most multiobjective evolutionary algorithms restrict the decision-maker to a single type of preference information. This article proposes an interactive version of MOEA/D that incorporates three types of preference information: reference points, preferred solutions, and preferred ranges. The reference vectors assigned to the solutions that met the preferences are kept in the population for the next iteration, while the remaining ones are re-arranged. The proposed method is applied to solve a river pollution problem to show its potential to support decision-makers in finding a satisfying solution without limiting them to a single type of preference. This gives more flexibility for the decision-maker to direct the search for the most preferred solution. In addition, we compared our method with interactive RVEA utilizing some benchmark problems.
YSO-asiasanat: monitavoiteoptimointi; optimointi; päätöksenteko; päätöksentukijärjestelmät; interaktiivisuus; evoluutiolaskenta; algoritmit
Vapaat asiasanat: multiobjective optimization; interactive decision making; evolutionary algorithms; continuous optimization
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2022
JUFO-taso: 1
