A4 Article in conference proceedings
Interactive MOEA/D with multiple types of preference information (2022)

Lárraga, G., & Miettinen, K. (2022). Interactive MOEA/D with multiple types of preference information. In J. E. Fieldsend (Ed.), GECCO '22 : Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1826-1834). ACM. https://doi.org/10.1145/3520304.3534013

JYU authors or editors

Publication details

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

Parent publication: GECCO '22 : Proceedings of the Genetic and Evolutionary Computation Conference Companion

Parent publication editors: Fieldsend, Jonathan E.

Place and date of conference: Boston, Massachusetts, USA, 9.-13.7.2022

ISBN: 978-1-4503-9268-6

Publication year: 2022

Publication date: 19/07/2022

Pages range: 1826-1834

Number of pages in the book: 2345

Publisher: ACM

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1145/3520304.3534013

Publication open access: Openly available

Publication channel open access: Partially open access channel

Abstract

Multiobjective optimization has the ultimate aim of helping a decision-maker to find a satisfying solution for a problem with multiple conflicting objectives. Usually, multiobjective evolutionary algorithms have been utilized to approximate the complete Pareto optimal set. However, some such algorithms incorporate preference information to direct the search toward a region of interest. Interactive methods allow decision-makers to learn more about the problem and update their preference information iteratively. Although preference information can be represented in multiple ways, most multiobjective evolutionary algorithms restrict the decision-maker to a single type of preference information. This article proposes an interactive version of MOEA/D that incorporates three types of preference information: reference points, preferred solutions, and preferred ranges. The reference vectors assigned to the solutions that met the preferences are kept in the population for the next iteration, while the remaining ones are re-arranged. The proposed method is applied to solve a river pollution problem to show its potential to support decision-makers in finding a satisfying solution without limiting them to a single type of preference. This gives more flexibility for the decision-maker to direct the search for the most preferred solution. In addition, we compared our method with interactive RVEA utilizing some benchmark problems.

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

Free keywords: multiobjective optimization; interactive decision making; evolutionary algorithms; continuous optimization

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 1