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

app.py

@@ -488,11 +488,10 @@ class GWO:
 
 
 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 = gwo.optimize()
+    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)
@@ -502,19 +501,12 @@ def optimize(npart, ndim, max_iter):
     dispersion = gwo.Dispersion()
     dispersion_text = f"Dispersion: {dispersion}"
 
-    # Get the contour plot as a PIL Image
-    contour_plot = gwo.plot_contour_and_wolves(best_positions_array)
-
     # 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))
 
-    # 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')
-
     # Format the output strings
     best_fitness_text = f"Best Fitness: {best_fitness_npy}"
     best_positions_text = f"Best Positions: {best_positions_npy}"