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Particle Swarm Optimization (PSO)

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

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

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

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

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