A4 Article in conference proceedings
A New Paradigm in Interactive Evolutionary Multiobjective Optimization (2020)


Saini, Bhupinder Singh; Hakanen, Jussi; Miettinen, Kaisa (2020). A New Paradigm in Interactive Evolutionary Multiobjective Optimization. In Bäck, Thomas; Preuss, Mike; Deutz, André; Wang, Hao; Doerr, Carola; Emmerich, Michael; Trautmann, Heike (Eds.) PPSN 2020 : 16th International Conference on Parallel Problem Solving from Nature, Lecture Notes in Computer Science, 12270. Cham: Springer, 243-256. DOI: 10.1007/978-3-030-58115-2_17


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Publication details

All authors or editors: Saini, Bhupinder Singh; Hakanen, Jussi; Miettinen, Kaisa

Parent publication: PPSN 2020 : 16th International Conference on Parallel Problem Solving from Nature

Parent publication editors: Bäck, Thomas; Preuss, Mike; Deutz, André; Wang, Hao; Doerr, Carola; Emmerich, Michael; Trautmann, Heike

Place and date of conference: Leiden, The Netherlands, 5.-9.9.2020

ISBN: 978-3-030-58114-5

eISBN: 978-3-030-58115-2

Journal or series: Lecture Notes in Computer Science

ISSN: 0302-9743

eISSN: 1611-3349

Publication year: 2020

Number in series: 12270

Pages range: 243-256

Number of pages in the book: 717

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: http://doi.org/10.1007/978-3-030-58115-2_17

Open Access: Publication channel is not openly available

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/71637


Abstract

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.


Keywords: optimisation; multi-objective optimisation; evolutionary computation; algorithms; decision support systems

Free keywords: interactive methods; achievement scalarizing functions; evolutionary algorithms; preference information; decision maker


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Preliminary JUFO rating: 1


Last updated on 2020-07-09 at 07:40