A New Paradigm in Interactive Evolutionary Multiobjective Optimization
| العنوان: | A New Paradigm in Interactive Evolutionary Multiobjective Optimization |
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| المؤلفون: | Jussi Hakanen, Kaisa Miettinen, Bhupinder Singh Saini |
| المساهمون: | Bäck, Thomas, Preuss, Mike, Deutz, André, Wang, Hao, Doerr, Carola, Emmerich, Michael, Trautmann, Heike |
| المصدر: | Parallel Problem Solving from Nature – PPSN XVI ISBN: 9783030581145 PPSN (2) |
| بيانات النشر: | Springer International Publishing, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | 050101 languages & linguistics, Mathematical optimization, Computer science, media_common.quotation_subject, decision maker, Evolutionary algorithm, päätöksentukijärjestelmät, evoluutiolaskenta, preference information, 02 engineering and technology, Space (commercial competition), Multi-objective optimization, optimointi, achievement scalarizing functions, algoritmit, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, Quality (business), evolutionary algorithms, Function (engineering), media_common, business.industry, 05 social sciences, interactive methods, Modular design, Decision maker, monitavoiteoptimointi, Preference, 020201 artificial intelligence & image processing, business |
| الوصف: | 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. peerReviewed |
| وصف الملف: | application/pdf; fulltext |
| ردمك: | 978-3-030-58114-5 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed63a25f6265973d6834ff773c5ecff9Test https://doi.org/10.1007/978-3-030-58115-2_17Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....ed63a25f6265973d6834ff773c5ecff9 |
| قاعدة البيانات: | OpenAIRE |
| ردمك: | 9783030581145 |
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