README.TXT - Particle Swarm Optimization Files

• /env: Virtual environment
• /Data: Input files not created endogenously by the scripts
• /Results: Files created endogenously by the scripts, either as intermediate or final output
• /Sandbox: Development code we don't want to push to the github repository
• /Coding: Deployment code
• /Archive: Old code that is now outdated

Coding:

• pso_componentsizing.py : most up-to-date
• gridSearchParallel.py :
• RadioSimulator.py :
• SimulateOnbeDesign_PassFail.ipynb : enter a set of parameters, and get a simple pass/fail output of whether that design worked.
• Grid Search and Simulation Plotter.ipynb : This is mostly a grid search, but happens to also have plotting capabilities. Does not have any PSO functionality.
• PSO_Parallel_ComponentSizing.ipynb:

Sandbox:

• PSO_Learning_Rastriggin.ipynb : My notes from learning the PSO algorithms, along with the implementation of PSO for Rastriggin's problem, based on a blog post with sample code.
• PSO_BasicProblem.ipynb : Particle Swarm optimization applied to the most basic problem: single-reservoir, just a few time steps.
• PSO_Parallel_Rastriggin.ipynb: Solution of Rastriggin's problem using parallelization to solve each particle's problem in parallel. Calculated the Did not time the execution to identify where the bottlenecks were, and did not

Workflow

• Run a grid search to identify feasible points
• Save the outcome of the grid search to a csv
• Sort the grid search csv to identify the best-performing feasible points

PSO:

• Define feasible space
• Randomly draw initial points and velocities
• Use the top n grid search items to populate some of the starting points for the particles
• For each epoch,
  • Pool particle simulations across the cores
  • Every n epochs, save the best location and all of the particle data.