Particle Swarm Optimization (PSO)

This repoistory contains research about Particle Swarm Optimization (PSO) and it's implementation to optimize Artificial Neural Network (ANN)

Including

• The Persentation of PSO
• The Paper of PSO
• Code for PSO is from: kuhess/pso-ann
• Used MNIST Dataset for Training ANN using PSO which you can download it from
  • The MNIST training-set
  • The MNIST test-set

Why Traning Neural Network with Particle Swarm Optimization instead of Gradient Descent

• Motivation

  • Gradient Descent requires differentiable activation function to calculate derivates making it slower than feedforward
  • To speed up backprop lot of memory is required to store activations
  • Backpropagation is strongly dependent on weights and biases initialization. A bad choice can lead to stagnation at local minima and so a suboptimal solution is found.
  • Backpropagation cannot make full use of parallelization due to its sequential nature

• Advantages of PSO

  • PSO does not require a differentiable or continous function
  • PSO leads to better convergence and is less likely to get stuck at local minima
  • Faster on GPU

Environment

• Windows 10
• AMD GPU radeon 530
• Python 3.9
• matplotlib 3.3
• numpy 1.19.5
• scikit-learn 0.24
• scipy 1.6.0

Run example with MNIST

$ git clone https://github.com/aboelkassem/PSO.git
$ cd PSO
$ python example_mnist.py

Demo and The Efficient of Results

the following diagram and reports shows the performance of testing data of the dataset including 10 classes (digits classes)