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Overcoming Local Optima for Determining 2-Optimality Consensus for Collectives

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Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12496))

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Abstract

Collective knowledge or consensus is used widely in our life. Determining the collective knowledge of a collective depends on the knowledge states of collective members. However, in a collective, each member has its knowledge, and knowledge states are often contradictory. Determining consensus satisfying postulate 2-Optimality of a collective is an NP-hard problem, and heuristic algorithms have been suggested. The basic algorithm is the most popular for this task. However, this algorithm can get stuck in local optima, which limits its consensus quality. To obtain consensus with high quality, in this study, we propose two approaches to avoid getting stuck in local optima. The experimental results show that the consensus quality generated by these approaches is at least 2.05% higher than that of the basic algorithm.

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Acknowledgment

This research was supported by the National Research Foundation of Korea (NRF) grant funded by the BK21PLUS Program (22A20130012009).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Yeungnam University, Gyeongsan, Republic of Korea

    Dai Tho Dang & Dosam Hwang

  2. Wroclaw University of Science and Technology, Wrocław, Poland

    Zygmunt Mazur

Authors
  1. Dai Tho Dang
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  2. Zygmunt Mazur
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  3. Dosam Hwang
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Corresponding author

Correspondence to Dosam Hwang .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Thua Thien Hue Center of Information Technology, Hue, Vietnam

    Bao Hung Hoang

  3. Vietnam - Korea University of Information and Communication Technology, University of Da Nang, Da Nang, Vietnam

    Cong Phap Huynh

  4. Department of Computer Engineering, Yeungnam University, Gyeungsan, Korea (Republic of)

    Dosam Hwang

  5. Department of Applied Informatics, Wrocław University of Science and Technology, Wroclaw, Poland

    Bogdan Trawiński

  6. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Dang, D.T., Mazur, Z., Hwang, D. (2020). Overcoming Local Optima for Determining 2-Optimality Consensus for Collectives. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-63007-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63006-5

  • Online ISBN: 978-3-030-63007-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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