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).

app.py

@@ -481,36 +481,39 @@ 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 = 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)
+    # 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 = self.Dispersion()
-        dispersion_text = f"Dispersion: {dispersion}"
+    # Calculate dispersion
+    dispersion = self.Dispersion()
+    dispersion_text = f"Dispersion: {dispersion}"
 
-        # Get the contour plot as a PIL Image
-        contour_plot = self.plot_contour_and_wolves(best_positions)
+    # Get the contour plot as a PIL Image
+    contour_plot = self.plot_contour_and_wolves(best_positions)
 
-        # Plot the dispersion over time
-        dispersion_plot = self.plot_dispersion()
+    # Plot the dispersion over time
+    dispersion_plot = self.plot_dispersion()
 
-        # Plot the dispersion heatmap
-        dispersion_heatmap_plot = self.plot_dispersion_heatmap(x_range=(-6,6), y_range=(-6,6))
+    # Plot the dispersion heatmap
+    dispersion_heatmap_plot = self.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')
+    # 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}"
+    # 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, dispersion_text
+    # Return the images and text
+    return [contour_plot, dispersion_plot, dispersion_heatmap_plot], best_fitness_text, best_positions_text, dispersion_text