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An Indicator Based Evolutionary Algorithm for Multiparty Multiobjective Knapsack Problems

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Intelligent Information Processing XII (IIP 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 703))

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Abstract

As a special case of the multiobjective optimization problem, the multiobjective knapsack problem (MOKP) widely exists in real-world applications. Currently, most algorithms used to solve MOKPs assume that these problems involve only one decision maker (DM). However, some complex MOKPs often involve more than one decision makers and we call such problems multiparty multiobjective knapsack problems (MPMOKPs). Existing algorithms cannot solve MPMOKPs effectively. To the best of our knowledge, there is only a little attention paid to MPMOKPs. In this paper, inspired by existing SMS-EMOA, we propose a novel indicator-based algorithm called SMS-MPEMOA to solve MPMOKPs, which aims to search solutions to satisfy all decision makers as much as possible. SMS-MPEMOA is compared with several state-of-the-art multiparty multiobjective optimization algorithms (MPMOEAs) on the benchmarks and the experimental results demonstrate that SMS-MPEMOA is very competitive.

This study is supported by the National Natural Science Foundation of China (Grant No. U23B2058), Shenzhen Fundamental Research Program (Grant No. JCYJ20220818102414030), the Major Key Project of PCL (Grant No. PCL2022A03), Shenzhen Science and Technology Program (Grant No. ZDSYS20210623091809029), Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies (Grant No. 2022B1212010005).

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, China

    Zhen Song, Wenjian Luo, Zipeng Ye & Kesheng Chen

  2. Peng Cheng Laboratory, Shenzhen, 518000, China

    Wenjian Luo

  3. School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Peilan Xu

Authors
  1. Zhen Song
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  2. Wenjian Luo
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  3. Peilan Xu
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  4. Zipeng Ye
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  5. Kesheng Chen
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Correspondence to Wenjian Luo .

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Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. University of Oslo, Oslo, Norway

    Jim Torresen

  3. De Montfort University, Leicester, UK

    Shengxiang Yang

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Song, Z., Luo, W., Xu, P., Ye, Z., Chen, K. (2024). An Indicator Based Evolutionary Algorithm for Multiparty Multiobjective Knapsack Problems. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 703. Springer, Cham. https://doi.org/10.1007/978-3-031-57808-3_17

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