A New Paradigm in Interactive Evolutionary Multiobjective Optimization
Saini, B. S., Hakanen, J., & Miettinen, K. (2020). A New Paradigm in Interactive Evolutionary Multiobjective Optimization. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, & H. Trautmann (Eds.), PPSN 2020 : 16th International Conference on Parallel Problem Solving from Nature (pp. 243-256). Springer. Lecture Notes in Computer Science, 12270. https://doi.org/10.1007/978-3-030-58115-2_17
Julkaistu sarjassa
Lecture Notes in Computer ScienceToimittajat
Päivämäärä
2020Oppiaine
TietotekniikkaMultiobjective Optimization GroupLaskennallinen tiedePäätöksen teko monitavoitteisestiMathematical Information TechnologyMultiobjective Optimization GroupComputational ScienceDecision analytics utilizing causal models and multiobjective optimizationTekijänoikeudet
© Springer Nature Switzerland AG 2020
Over the years, scalarization functions have been used to solve multiobjective optimization problems by converting them to one or more single objective optimization problem(s). This study proposes a novel idea of solving multiobjective optimization problems in an interactive manner by using multiple scalarization functions to map vectors in the objective space to a new, so-called preference incorporated space (PIS). In this way, the original problem is converted into a new multiobjective optimization problem with typically fewer objectives in the PIS. This mapping enables a modular incorporation of decision maker’s preferences to convert any evolutionary algorithm to an interactive one, where preference information is directing the solution process. Advantages of optimizing in this new space are discussed and the idea is demonstrated with two interactive evolutionary algorithms: IOPIS/RVEA and IOPIS/NSGA-III. According to the experiments conducted, the new algorithms provide solutions that are better in quality as compared to those of state-of-the-art evolutionary algorithms and their variants where preference information is incorporated in the original objective space. Furthermore, the promising results require fewer function evaluations.
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Julkaisija
SpringerEmojulkaisun ISBN
978-3-030-58114-5Konferenssi
International Conference on Parallel Problem Solving From NatureKuuluu julkaisuun
PPSN 2020 : 16th International Conference on Parallel Problem Solving from NatureISSN Hae Julkaisufoorumista
0302-9743Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/36258380
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Akatemiahanke, SA; Profilointi, SALisätietoja rahoituksesta
This research was supported by the Academy of Finland (grant numbers 322221 and 311877). The research is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyväskylä.Lisenssi
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