Swing Curve Optimization by Differential Evolution Algorithm

Differential Evolution (DE) algorithm to optimize parameters for a swing curve simulation.

The program uses the DE algorithm, a robust evolutionary optimization technique, to find the best parameter values (pm, pm1, pm2, pm3) for the swing curve simulation. The objective is to achieve a specific target angle and time during the fault clearance event. The DE algorithm evolves a population of candidate solutions over multiple generations, exploring the parameter space to converge to an optimal solution.

The main steps of the program include:

1. Initializing the DE algorithm parameters and the target angle and time.
2. Setting up the swing curve simulation with initial parameter values.
3. Implementing the DE algorithm's main loop, including mutation, crossover, and selection operations.
4. Evaluating the fitness of each candidate solution based on the swing curve's performance.
5. Updating the population and best individual based on fitness evaluations.
6. Displaying the optimized parameter values that best achieve the target angle and time.
7. Performing the swing curve simulation using the optimized parameters and plotting the results.