A generic particle swarm optimization (PSO) Python class.
Requires NumPy.
PSO is a minimizing algorithm, so ensure that your fitness function is written accordingly. A common technique to maximize a function with PSO is to return a negative value.
Hard parameter bounds and constraints are implemented.
For a comprehensive walkthrough of how to use the Optimizer class, see the Golinski example.
For an example of using the Optimizer class to tune a simple feedforward neural network, see the Iris example.
Note that other optimization techniques such as gradient descent may be more suitable for training large networks; the iris example is more of a proof-of-concept. On the other hand, PSO is useful for problems that are not differentiable.