Artificial Decision Maker Driven by PSO : An Approach for Testing Reference Point Based Interactive Methods
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
Published inLecture Notes in Computer Science
© Springer International Publishing AG, part of Springer Nature 2018.
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. ...
PublisherSpringer International Publishing
Parent publication ISBN978-3-319-99252-5
ConferenceInternational Conference on Parallel Problem Solving From Nature
Is part of publicationParallel Problem Solving from Nature - PPSN XV : 15th International Conference, Coimbra, Portugal, September 8–12, 2018, Proceedings, Part 1
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF
Additional information about fundingThis work was partially funded by Grants TIN2017-86049-R (Spanish MICINN) and P12-TIC-1519 (PAIDI). C. Barba-González was supported by Grant BES-2015-072209 (Spanish MICINN) and University of Jyväskylä. J. García-Nieto is the recipient Post-Doct fellowship of “Plan Propio” at Universidad de Málaga. This work was supported on the part of V. Ojalehto by the Academy of Finland (grant number 287496).
Showing items with similar title or keywords.
Misitano, Giovanni; Saini, Bhupinder Singh; Afsar, Bekir; Shavazipour, Babooshka; Miettinen Kaisa (Institute of Electrical and Electronics Engineers (IEEE), 2021)Interactive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the ...
Data-driven Interactive Multiobjective Optimization : Challenges and a Generic Multi-agent Architecture Afsar, Bekir; Podkopaev, Dmitry; Miettinen, Kaisa (Elsevier BV, 2020)In many decision making problems, a decision maker needs computer support in finding a good compromise between multiple conflicting objectives that need to be optimized simultaneously. Interactive multiobjective optimization ...
INFRINGER : a novel interactive multi-objective optimization method able to learn a decision maker’s preferences utilizing machine learning Misitano, Giovanni (2020)Tässä tutkielmassa kehitetään interaktiivinen menetelmä – nimeltään INFRINGER – monitavoiteoptimoinnin ongelmien ratkaisemisen tueksi. Menetelmä kykenee oppimaan päätöksentekijän mieltymykset (preferenssit), ja esittää ...
LR-NIMBUS : an interactive algorithm for uncertain multiobjective optimization with lightly robust efficient solutions Koushki, Javad; Miettinen, Kaisa; Soleimani-damaneh, Majid (Springer Science and Business Media LLC, 2022)In this paper, we develop an interactive algorithm to support a decision maker to find a most preferred lightly robust efficient solution when solving uncertain multiobjective optimization problems. It extends the interactive ...
Interactivized : Visual Interaction for Better Decisions with Interactive Multiobjective Optimization Hakanen, Jussi; Radoš, Sanjin; Misitano, Giovanni; Saini, Bhupinder S.; Miettinen, Kaisa; Matković, Krešimir (IEEE, 2022)In today’s data-driven world, decision makers are facing many conflicting objectives. Since there is usually no solution that optimizes all objectives simultaneously, the aim is to identify a solution with acceptable ...