A4 Article in conference proceedings
Artificial Decision Maker Driven by PSO : An Approach for Testing Reference Point Based Interactive Methods (2018)


Barba-González, C., Ojalehto, V., García-Nieto, J., Nebro, A. J., Miettinen, K., & Aldana-Montes, J. F. (2018). Artificial Decision Maker Driven by PSO : An Approach for Testing Reference Point Based Interactive Methods. In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete, & D. Whitley (Eds.), Parallel Problem Solving from Nature - PPSN XV : 15th International Conference, Coimbra, Portugal, September 8–12, 2018, Proceedings, Part 1 (pp. 274-285). Springer International Publishing. Lecture Notes in Computer Science, 11101. https://doi.org/10.1007/978-3-319-99253-2_22


JYU authors or editors


Publication details

All authors or editors: Barba-González, Cristóbal; Ojalehto, Vesa; García-Nieto, José; Nebro, Antonio J.; Miettinen, Kaisa; Aldana-Montes, José F.

Parent publication: Parallel Problem Solving from Nature - PPSN XV : 15th International Conference, Coimbra, Portugal, September 8–12, 2018, Proceedings, Part 1

Parent publication editors: Auger, Anne; Fonseca, Carlos M.; Lourenço, Nuno; Machado, Penousal; Paquete, Luís; Whitley, Darrell

ISBN: 978-3-319-99252-5

Journal or series: Lecture Notes in Computer Science

ISSN: 0302-9743

eISSN: 1611-3349

Publication year: 2018

Number in series: 11101

Pages range: 274-285

Publisher: Springer International Publishing

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-319-99253-2_22

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/59501


Abstract

Over the years, many interactive multiobjective optimization methods based on a reference point have been proposed. With a reference point, the decision maker indicates desirable objective function values to iteratively direct the solution process. However, when analyzing the performance of these methods, a critical issue is how to systematically involve decision makers. A recent approach to this problem is to replace a decision maker with an artificial one to be able to systematically evaluate and compare reference point based interactive methods in controlled experiments. In this study, a new artificial decision maker is proposed, which reuses the dynamics of particle swarm optimization for guiding the generation of consecutive reference points, hence, replacing the decision maker in preference articulation. We use the artificial decision maker to compare interactive methods. We demonstrate the artificial decision maker using the DTLZ benchmark problems with 3, 5 and 7 objectives to compare R-NSGA-II and WASF-GA as interactive methods. The experimental results show that the proposed artificial decision maker is useful and efficient. It offers an intuitive and flexible mechanism to capture the current context when testing interactive methods for decision making.


Keywords: multi-objective optimisation; decision making; optimisation

Free keywords: multiobjective optimization; preference articulation; multiple criteria decision making; particle swarm optimization


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Ministry reporting: Yes

Reporting Year: 2018

JUFO rating: 1


Last updated on 2021-16-07 at 10:46