A4 Artikkeli konferenssijulkaisussa
A General Architecture for Generating Interactive Decomposition-Based MOEAs (2022)
Lárraga, G., & Miettinen, K. (2022). A General Architecture for Generating Interactive Decomposition-Based MOEAs. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII : 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part II (pp. 81-95). Springer International Publishing. Lecture Notes in Computer Science, 13398. https://doi.org/10.1007/978-3-031-14721-0_6
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Lárraga, Giomara; Miettinen, Kaisa
Emojulkaisu: Parallel Problem Solving from Nature – PPSN XVII : 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part II
Emojulkaisun toimittajat: Rudolph, Günter; Kononova, Anna V.; Aguirre, Hernán; Kerschke, Pascal; Ochoa, Gabriela; Tušar, Tea
Konferenssin paikka ja aika: Dortmund, Germany, 10.-14.9.2022
ISBN: 978-3-031-14720-3
eISBN: 978-3-031-14721-0
Lehti tai sarja: Lecture Notes in Computer Science
ISSN: 0302-9743
eISSN: 1611-3349
Julkaisuvuosi: 2022
Ilmestymispäivä: 15.08.2022
Sarjan numero: 13398
Artikkelin sivunumerot: 81-95
Kirjan kokonaissivumäärä: 619
Kustantaja: Springer International Publishing
Kustannuspaikka: Cham
Julkaisumaa: Sveitsi
Julkaisun kieli: englanti
DOI: https://doi.org/10.1007/978-3-031-14721-0_6
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/83384
Tiivistelmä
Evolutionary algorithms have been widely applied for solving multiobjective optimization problems. Such methods can approximate many Pareto optimal solutions in a population. However, when solving real-world problems, a decision maker is usually involved, who may only be interested in a subset of solutions that meet their preferences. Several methods have been proposed to consider preference information during the solution process. Among them, interactive methods support the decision maker in learning about the trade-offs among objectives and the feasibility of solutions. Also, such methods allow the decision maker to provide preference information iteratively. Typically, interactive multiobjective evolutionary algorithms are modifications of existing a priori or a posteriori algorithms. However, they mainly focus on finding a region of interest and do not support the decision maker finding the most preferred solution. In addition, the cognitive load imposed on the decision maker is usually not considered. This article proposes an architecture for developing interactive decomposition-based evolutionary algorithms that can support the decision maker during the solution process. The proposed architecture aims to improve the applicability of interactive methods in solving real-world problems by considering the needs of a decision maker. We apply our proposal to generate an interactive decomposition-based algorithm utilizing a reference vector re-arrangement procedure and MOEA/D. We demonstrate the performance of our proposal with a real-world problem and multiple benchmark problems.
YSO-asiasanat: optimointi; monitavoiteoptimointi; evoluutiolaskenta; päätöksentukijärjestelmät; interaktiivisuus
Vapaat asiasanat: multiobjective optimization; evolutionary algorithms; preference information; decision making; interactive methods; interactive preference incorporation
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2022
JUFO-taso: 1
