Updated the optimize method with removal of contour_plot functionality. Updated plot_contour_and_wolves method lines 344-346 with: best_positions_loaded_array = np.load('best_positions_array.npy') wolf_positions = best_positions_loaded_array. And modified the optimize function with: # Load best_positions_loaded_array best_positions_loaded_array = np.load('best_positions_array.npy') # Plot the contour_and_wolves plot_contour_and_wolves = gwo.plot_contour_and_wolves(best_positions_loaded_array) Removed the contour_plot from gwo.optimize also only the other two variables remain

Updated lines 490-515 with: def optimize(npart, ndim, max_iter): # Initialize the GWO algorithm with the provided parameters gwo = GWO(obj=obj, npart=npart, ndim=ndim, max_iter=max_iter, init=init, bounds=bounds) best_positions, best_fitness, contour_plot = gwo.optimize() # Convert best_fitness and best_positions to NumPy arrays if necessary best_fitness_npy = np.array(best_fitness) best_positions_npy = np.array(best_positions) # Calculate dispersion dispersion = gwo.Dispersion() dispersion_text = f"Dispersion: {dispersion}" # Plot the dispersion over time dispersion_plot = gwo.plot_dispersion() # Plot the dispersion heatmap dispersion_heatmap_plot = gwo.plot_dispersion_heatmap(x_range=(-6,6), y_range=(-6,6)) # Format the output strings best_fitness_text = f"Best Fitness: {best_fitness_npy}" best_positions_text = f"Best Positions: {best_positions_npy}" # Return the images and text return [contour_plot, dispersion_plot, dispersion_heatmap_plot], best_fitness_text, best_positions_text, best_fitness_npy, best_positions_npy, dispersion_text

Updated line 506 with: contour_plot = gwo.plot_contour_and_wolves(best_positions_array)

Updated lines 343-346 with: # Ensure wolf_positions is a 2D array wolf_positions = np.array(wolf_positions) if wolf_positions.ndim == 1: wolf_positions = wolf_positions.reshape(-1, 1)

Updated lines 204-234 with: def optimize(self): """ Run a full optimization and return the best positions and fitness values. This method is designed to be used with Gradio. """ # Initialize the swarm self.Initialize() # Lists to store the best positions and fitness values at each step best_positions = [] best_fitness = [] # Main loop while not self.Done(): self.Step() # Perform an optimization step # Update best_positions and best_fitness with the current best values best_positions.append(self.gbest[-1]) best_fitness.append(self.gpos[-1]) # Convert the list of best positions to a NumPy array best_positions_array = np.array(best_positions) # Get the contour plot as a PIL Image contour_plot = self.plot_contour_and_wolves(best_positions_array) # Print the best positions and fitness found print("Best Positions:", best_positions) print("Best Fitness:", best_fitness) # Return the best positions and fitness after the optimization return best_positions, best_fitness, contour_plot

Updated lines 340-341 with: best_positions_npy = np.load(‘best_positions.npy’) [removed] and wolf_positions = best_positions_npy [removed]. Generating a PIL Image population deriving from gr.components.Image()

Ignore previous notation. Updated lines 488, 495, 499, 502 and 505 with: gwo . Also, udated line 516 with: return [contour_plot, dispersion_plot, dispersion_heatmap_plot], best_fitness_text, best_positions_text, best_fitness_npy, best_positions_npy, dispersion_text

Updated lines 488, 499, 502, 505 with: # Initialize the GWO algorithm with the provided parameters gwo = GWO(obj=obj, npart=npart, ndim=ndim, max_iter=max_iter, init=init, bounds=bounds)

Updated lines 486-487 with: # Initialize the GWO algorithm with the provided parameters gwo = GWO(obj=obj, npart=npart, ndim=ndim, max_iter=max_iter, init=init, bounds=bounds).

Updated lines 483 and 485 with: def optimize(npart, ndim, max_iter): and best_positions, best_fitness = gwo.optimize()

Updated lines 483-516 with: new optimize function modifications

Updated lines 515-517 with: # Load the best fitness and positions from .npy files, best_fitness_npy = np.load('best_fitness.npy’), best_positions_npy = np.load('best_positions.npy')

Updated line 517 with: removal of best_fitness_npy, best_positions_npy,

Updated line 414 with: dispersion_values = np.array([self.Dispersion() for _ in positions]) [plot_dispersion_heatmap] method

Updated line 4 with: from matplotlib.colors import LogNorm Updated lines 453-479 with: def plot_dispersion_heatmap(self, x_range, y_range, resolution=100): # Create a grid of points within the specified range x = np.linspace(*x_range, resolution) y = np.linspace(*y_range, resolution) X, Y = np.meshgrid(x, y) positions = np.vstack([X.ravel(), Y.ravel()]).T # Calculate the dispersion for each position in the grid dispersion_values = np.array([self.Dispersion(pos) for pos in positions]) Z = dispersion_values.reshape(X.shape) # Plot the dispersion heatmap plt.figure(figsize=(10, 8)) plt.pcolormesh(X, Y, Z, cmap='viridis', norm=LogNorm()) plt.colorbar(label='Dispersion') # Set plot title and labels plt.title('Dispersion Heatmap') plt.xlabel('x') plt.ylabel('y') # Convert the plot to a PIL Image and return it buf = BytesIO() plt.savefig(buf, format='png') buf.seek(0) plt.close() # Close the figure to free up memory return Image.open(buf)

Updated lines 393-403 with: def Dispersion(self): """Calculate the dispersion of the swarm""" # Ensure self.gpos is a NumPy array if not isinstance(self.gpos, np.ndarray): self.gpos = np.array(self.gpos) # Now self.gpos should be a NumPy array, so we can calculate the dispersion x, y = self.gpos[:, 0], self.gpos[:, 1] dx = x.max() - x.min() dy = y.max() - y.min() return (dx + dy) / 2.0

Updated lines 388-392 with: def Dispersion(self): # """Calculate the dispersion of the swarm""" dispersion = np.std(self.gpos[:, 0]) + np.std(self.gpos[:, 1]) return dispersion

Updated line 340 with: best_positions_npy = np.load('best_positions.npy’). Updated line 358 with: contour = plt.contour(X, Y, Z, levels=20, cmap='magma’). Updated line 455 with: contour_plot = gwo.plot_contour_and_wolves(best_positions)

Create app.py

init!

initial commit