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Uniform Initialization in Response Space for PSO and its Applications

Author

Listed:
  • Ji, Kaipeng
  • Zhao, Peng
  • Zhou, Xiaowei
  • Chen, Yuhong
  • Dong, Zhengyang
  • Zheng, Jianguo
  • Fu, Jianzhong
  • Zhou, Huamin

Abstract

Particle swarm optimization (PSO) is widely used in the parameter estimation for complex models, which is the key to establishing a mathematical model. However, convergence to the local optimal easily occurs in PSO. A substantial amount of researches have been conducted to improve the evolutionary process of PSO; nonetheless, the study on initialization method is relatively limited. Generally, initial particle positions are uniformly distributed in the parameter space but unevenly distributed in the response space for nonlinear models, which can hinder optimization. In this paper, a novel initialization method for PSO with an uninformative prior of parameters in the model (UPPSO) is proposed. The method initializes particle positions uniformly distributed in the response space and makes the effect of particle velocity uniform in the response space. The UPPSO and other five algorithms were applied to estimate parameters for three different polymer models, that is, viscosity and PVT (pressure-volume-temperature) models which are very important and must be estimated for each type of polymers. In comparison with other algorithms, the optimization capacity of UPPSO for each model was ranked second, and UPPSO was particularly outstanding in obtaining the optimal parameter. Moreover, UPPSO was also competitive in computation performance. In general, UPPSO is conducive to optimization, efficient and adaptable, and it can also be generalized to other swarm intelligence algorithms, such as the differential evolution (DE) and artificial bee colony (ABC), etc.

Suggested Citation

  • Ji, Kaipeng & Zhao, Peng & Zhou, Xiaowei & Chen, Yuhong & Dong, Zhengyang & Zheng, Jianguo & Fu, Jianzhong & Zhou, Huamin, 2022. "Uniform Initialization in Response Space for PSO and its Applications," Applied Mathematics and Computation, Elsevier, vol. 431(C).
  • Handle: RePEc:eee:apmaco:v:431:y:2022:i:c:s0096300322004258
    DOI: 10.1016/j.amc.2022.127351
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    References listed on IDEAS

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    1. Wang, Xianjia & Lv, Shaojie, 2019. "The roles of particle swarm intelligence in the prisoner’s dilemma based on continuous and mixed strategy systems on scale-free networks," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 213-220.
    2. Cheng, Maolin & Han, Yun, 2020. "Application of a modified CES production function model based on improved PSO algorithm," Applied Mathematics and Computation, Elsevier, vol. 387(C).
    3. Garg, Harish, 2016. "A hybrid PSO-GA algorithm for constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 292-305.
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