Particle Swarm Optimization From Scratch Using Python

• GitHub: https://github.com/iphysresearch/PSO_python_demo
• Slide: https://slides.com/iphysresearch/pso
• Video: https://www.bilibili.com/video/BV1kv411h7sC/

Mock Data Challenge

• Given
  • Training data containing only noise: TrainingData.mat
  • Noise is Gaussian, stationary
  • Data to be analysed: analysisData.mat
  • Signal: Quadratic chirp
  • Parameter search ranges:
    • 40 < a1 < 100
    • 1 < a2 < 50
    • 11 < a3 < 15
• Detection and Estimation
  • Detection: Is there a signal in the analysis data?
  • Estimation: If so, estimate its parameters
  • Use PSO to obtain generalized likelihood ratio test (GLRT) and maximum likelihood estimate (MLE)
  • Recommended PSO parameters:
    • Best of 8 runs
    • Termination at 2000 iterations

Pre-requirements

• Python 3.x
• Numpy
• Scipy
• tqdm

How to use it

Just run demo.py script or demo.ipynb.

$ python demo.py

Reference

• 2021 Gravitational Wave Data Analysis School in China (Soumya D. Mohanty)

  • https://github.com/mohanty-sd/GWSC
  • https://github.com/BNUGW/GWSC21

• Particle Swarm Optimization Visually Explained

Licence

• MIT