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Optimal sizing of a supercapacitor/battery energy harvesting system using particle swarm optimization

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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  • /Archive: Old code that is now outdated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.

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Optimal sizing of a supercapacitor/battery energy harvesting system using particle swarm optimization

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