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Set-based particle swarm optimization with status memory for knapsack problem

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Abstract

Set-based particle swarm optimization (S-PSO) operates on discrete space. S-PSO can solve combinatorial optimization problem with high quality and is successful to apply to the large-scale problem. In S-PSO, a velocity is a set with possibility and a position is a candidate solution. In this paper, we present a novel algorithm of set-based particle swarm optimization with status memory (S-PSOSM) to decide the position based on the previous position for solving knapsack problem. Some operators are redefined for S-PSOSM. S-PSOSM is a simple algorithm because the state of probability reduces. In addition, the weight of S-PSOSM is discussed. S-PSOSM shows high qualities in experimental results.

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Authors and Affiliations

  1. Fukuoka Institute of Technology, Fukuoka, Japan

    Takahiro Hino, Sota Ito, Tao Liu & Michiharu Maeda

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  1. Takahiro Hino
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  2. Sota Ito
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  3. Tao Liu
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  4. Michiharu Maeda
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Correspondence to Michiharu Maeda.

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Hino, T., Ito, S., Liu, T. et al. Set-based particle swarm optimization with status memory for knapsack problem. Artif Life Robotics 21, 98–105 (2016). https://doi.org/10.1007/s10015-015-0253-6

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  • DOI: https://doi.org/10.1007/s10015-015-0253-6

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