A4 Article in conference proceedings
A General Architecture for Generating Interactive Decomposition-Based MOEAs (2022)

Lárraga, G., & Miettinen, K. (2022). A General Architecture for Generating Interactive Decomposition-Based MOEAs. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII : 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part II (pp. 81-95). Springer International Publishing. Lecture Notes in Computer Science, 13398. https://doi.org/10.1007/978-3-031-14721-0_6

JYU authors or editors

Publication details

All authors or editors: Lárraga, Giomara; Miettinen, Kaisa

Parent publication: Parallel Problem Solving from Nature – PPSN XVII : 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part II

Parent publication editors: Rudolph, Günter; Kononova, Anna V.; Aguirre, Hernán; Kerschke, Pascal; Ochoa, Gabriela; Tušar, Tea

Place and date of conference: Dortmund, Germany, 10.-14.9.2022

ISBN: 978-3-031-14720-3

eISBN: 978-3-031-14721-0

Journal or series: Lecture Notes in Computer Science

ISSN: 0302-9743

eISSN: 1611-3349

Publication year: 2022

Publication date: 15/08/2022

Number in series: 13398

Pages range: 81-95

Number of pages in the book: 619

Publisher: Springer International Publishing

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-031-14721-0_6

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/83384

Abstract

Evolutionary algorithms have been widely applied for solving multiobjective optimization problems. Such methods can approximate many Pareto optimal solutions in a population. However, when solving real-world problems, a decision maker is usually involved, who may only be interested in a subset of solutions that meet their preferences. Several methods have been proposed to consider preference information during the solution process. Among them, interactive methods support the decision maker in learning about the trade-offs among objectives and the feasibility of solutions. Also, such methods allow the decision maker to provide preference information iteratively. Typically, interactive multiobjective evolutionary algorithms are modifications of existing a priori or a posteriori algorithms. However, they mainly focus on finding a region of interest and do not support the decision maker finding the most preferred solution. In addition, the cognitive load imposed on the decision maker is usually not considered. This article proposes an architecture for developing interactive decomposition-based evolutionary algorithms that can support the decision maker during the solution process. The proposed architecture aims to improve the applicability of interactive methods in solving real-world problems by considering the needs of a decision maker. We apply our proposal to generate an interactive decomposition-based algorithm utilizing a reference vector re-arrangement procedure and MOEA/D. We demonstrate the performance of our proposal with a real-world problem and multiple benchmark problems.

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

Free keywords: multiobjective optimization; evolutionary algorithms; preference information; decision making; interactive methods; interactive preference incorporation

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 1