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Particle Swarm Optimization Based on Pairwise Comparisons

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Book cover Advances in Swarm Intelligence (ICSI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10941))

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Abstract

Particle swarm optimization (PSO) is a widely-adopted optimization algorithm which is based on particles’ fitness evaluations and their swarm intelligence. However, it is difficult to obtain the exact fitness evaluation value and is only able to compare particles in a pairwise manner in many real applications such as dose selection, tournament, crowdsourcing and recommendation. Such ordinal preferences from pairwise comparisons instead of exact fitness evaluations lead the traditional PSO to fail. This paper proposes a particle swarm optimization based on pairwise comparisons. Experiments show that the proposed method enables the traditional PSO to work well by using only ordinal preferences from pairwise comparisons.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tongji University, Shanghai, China

    JunQi Zhang, JianQing Chen, XiXun Zhu & ChunHui Wang

  2. Key Laboratory of Embedded System and Service Computing, Ministry of Education, Shanghai, China

    JunQi Zhang, JianQing Chen, XiXun Zhu & ChunHui Wang

Authors
  1. JunQi Zhang
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  2. JianQing Chen
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  3. XiXun Zhu
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  4. ChunHui Wang
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Corresponding author

Correspondence to JunQi Zhang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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Zhang, J., Chen, J., Zhu, X., Wang, C. (2018). Particle Swarm Optimization Based on Pairwise Comparisons. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-93815-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93814-1

  • Online ISBN: 978-3-319-93815-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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