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Prediction of equipment performance index based on improved chaotic lion swarm optimization–LSTM

  • Zhe Yang
  • Chunwu WeiEmail author
Methodologies and Application
  • 1 Downloads

Abstract

The lion swarm optimizer (LSO) algorithm is a novel meta-heuristic, inspired from the social behavior of lions. This paper introduces the chaos theory into the LSO algorithm with the aim of accelerating its global convergence speed. First, detailed studies are carried out on standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the improved chaotic lion swarm optimization algorithm is compared with the traditional LSO and some other popular meta-heuristics algorithms. Lastly, this paper uses the improved chaotic lion swarm algorithm to further optimize the LSTM super-parameters for the problem of equipment life prediction. In addition, for the validity of the analysis method, the comparative experiments of several typical time series prediction models and different parameter optimization algorithms are carried out to verify the proposed methods in each part, which proves that the improved chaotic lion group–LSTM model has strong generalization ability and higher accuracy in equipment life prediction.

Keywords

Long short-term memory (LSTM) recurrent neural network Chaotic mapping Hyperparameter optimization Improved chaotic lion swarm optimization algorithm Deep learning 

Notes

Funding

This study was not funded by any other organization.

Compliance with ethical standards

Conflict of interest

Chunwu Wei and Zhe Yang declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK
  2. 2.Graduate School of Information and Computer ScienceTaiyuan University of TechnologyTaiyuanChina

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