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Article


Title

Interactive co-evolutionary multiple objective optimization algorithms for finding consensus solutions for a group of Decision Makers

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2022

Published in

Information Sciences

Journal year: 2022 | Journal volume: vol. 616

Article type

scientific article

Publication language

english

Keywords
EN
  • Evolutionary multiple objective optimization
  • Co-evolution
  • Decomposition
  • Indirect preference information
  • Preference learning
  • Group decision making
Abstract

EN We introduce interactive algorithms for identifying consensus solutions to multiple objective optimization problems. They learn the Decision Makers’ (DMs’) preferences from indirect judgments and represent the recognized aspirations using scalar optimization goals. Their role is to set guidelines for the evolution and discovery of consensuses. The novelty of our proposals lies in co-evolving two populations: primary and supportive. The former’s role is to discover solutions relevant to the committee. The latter approximates the entire Pareto front, revealing a variety of trade-offs between objectives. The method improves the potential of conducting more informative interactions with the DMs and prevents the evolution from stagnation. Furthermore, it improves the evolutionary pace by dynamically removing no longer worthwhile goals from the supportive population in favor of increasing the primary population size. We confirm the importance of using co-evolution and dynamic resource allocation in extensive experiments. Also, we prove the competitiveness of our proposals by comparing them with the state-of-the-art methods on the WFG benchmarks. Finally, we demonstrate their practical usefulness when applied to the real-world problem of designing an environmentally friendly supply chain.

Date of online publication

18.10.2022

Pages (from - to)

157 - 181

DOI

10.1016/j.ins.2022.10.064

URL

https://www.sciencedirect.com/science/article/pii/S002002552201177X?via%3Dihub

License type

CC BY (attribution alone)

Open Access Mode

czasopismo hybrydowe

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

200.0

Impact Factor

8.233 [List 2021]

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