in this code, can you make the solving algorithm work like this: it calculates every single possible position and gives the most less and optimal solution. code: import pygame
import random
import sys
from collections import deque

# Initialize Pygame
pygame.init()

# Constants
CELL_SIZE = 25          # Size of each cell in pixels
MAZE_SIZE = 24          # 24x24 maze
WIDTH, HEIGHT = CELL_SIZE * MAZE_SIZE, CELL_SIZE * MAZE_SIZE
WINDOW = pygame.display.set_mode((WIDTH, HEIGHT))
pygame.display.set_caption("test")

# Colors
WHITE = (255, 255, 255)  # Walls
BLACK = (0, 0, 0)        # Path
GREEN = (0, 255, 0)      # Solution path
RED = (255, 0, 0)        # Start
BLUE = (0, 0, 255)       # Exit

# Directions and their opposite
DIRS = {
    'N': (0, -1),
    'S': (0, 1),
    'E': (1, 0),
    'W': (-1, 0)
}
OPPOSITE = {'N': 'S', 'S': 'N', 'E': 'W', 'W': 'E'}

class Cell:
    """Represents each cell in the maze."""
    def __init__(self, x, y):
        self.x = x
        self.y = y
        self.walls = {'N': True, 'S': True, 'E': True, 'W': True}
        self.visited = False

    def draw(self, surface, color=BLACK):
        """Draws the cell walls and fills the cell with the specified color."""
        x = self.x * CELL_SIZE
        y = self.y * CELL_SIZE

        # Fill the cell background
        pygame.draw.rect(surface, color, (x, y, CELL_SIZE, CELL_SIZE))

        # Draw walls
        if self.walls['N']:
            pygame.draw.line(surface, WHITE, (x, y), (x + CELL_SIZE, y), 2)
        if self.walls['S']:
            pygame.draw.line(surface, WHITE, (x, y + CELL_SIZE), (x + CELL_SIZE, y + CELL_SIZE), 2)
        if self.walls['E']:
            pygame.draw.line(surface, WHITE, (x + CELL_SIZE, y), (x + CELL_SIZE, y + CELL_SIZE), 2)
        if self.walls['W']:
            pygame.draw.line(surface, WHITE, (x, y), (x, y + CELL_SIZE), 2)

def remove_walls(current, next_cell, direction):
    """Removes the walls between two adjacent cells."""
    current.walls[direction] = False
    next_cell.walls[OPPOSITE[direction]] = False

def generate_maze(grid, start_cell):
    """
    Generates the maze using the Depth-First Search (DFS) algorithm with iterative backtracking.
    This creates a maze with many turns and dead ends.
    """
    stack = []
    current = start_cell
    current.visited = True
    stack.append(current)

    while stack:
        current = stack[-1]
        x, y = current.x, current.y

        # Get list of unvisited neighbors
        neighbors = []
        for direction, (dx, dy) in DIRS.items():
            nx, ny = x + dx, y + dy
            if 0 <= nx < MAZE_SIZE and 0 <= ny < MAZE_SIZE:
                neighbor = grid[ny][nx]
                if not neighbor.visited:
                    neighbors.append((direction, neighbor))

        if neighbors:
            # Choose a random unvisited neighbor to ensure randomness and complexity
            direction, next_cell = random.choice(neighbors)
            remove_walls(current, next_cell, direction)
            next_cell.visited = True
            stack.append(next_cell)
        else:
            # Backtrack if no unvisited neighbors
            stack.pop()

def set_start_and_exit(grid):
    """
    Randomly selects start and exit points on opposite boundaries.
    Ensures that they are perfectly aligned with the maze boundaries.
    """
    # Define possible boundaries
    boundaries = ['N', 'S', 'E', 'W']
    side_start = random.choice(boundaries)
    opposite_sides = {'N': 'S', 'S': 'N', 'E': 'W', 'W': 'E'}
    side_exit = opposite_sides[side_start]

    # Select random positions on the chosen boundaries
    if side_start == 'N':
        start_x = random.randint(0, MAZE_SIZE - 1)
        start_y = 0
    elif side_start == 'S':
        start_x = random.randint(0, MAZE_SIZE - 1)
        start_y = MAZE_SIZE - 1
    elif side_start == 'E':
        start_x = MAZE_SIZE - 1
        start_y = random.randint(0, MAZE_SIZE - 1)
    else:  # 'W'
        start_x = 0
        start_y = random.randint(0, MAZE_SIZE - 1)

    start_cell = grid[start_y][start_x]
    # Remove the wall for the start cell
    start_cell.walls[side_start] = False

    # Set exit on the opposite side
    if side_exit == 'N':
        exit_x = random.randint(0, MAZE_SIZE - 1)
        exit_y = 0
    elif side_exit == 'S':
        exit_x = random.randint(0, MAZE_SIZE - 1)
        exit_y = MAZE_SIZE - 1
    elif side_exit == 'E':
        exit_x = MAZE_SIZE - 1
        exit_y = random.randint(0, MAZE_SIZE - 1)
    else:  # 'W'
        exit_x = 0
        exit_y = random.randint(0, MAZE_SIZE - 1)

    exit_cell = grid[exit_y][exit_x]
    # Remove the wall for the exit cell
    exit_cell.walls[side_exit] = False

    return start_cell, exit_cell

def find_optimal_path(grid, start_cell, exit_cell):
    """
    Uses Breadth-First Search (BFS) to find the shortest path from start to exit.
    Returns the path as a list of cells from start to exit.
    """
    queue = deque()
    queue.append(start_cell)
    predecessors = {start_cell: None}
    visited = set()
    visited.add(start_cell)

    while queue:
        current = queue.popleft()

        if current == exit_cell:
            break

        for direction, (dx, dy) in DIRS.items():
            if not current.walls[direction]:
                nx, ny = current.x + dx, current.y + dy
                if 0 <= nx < MAZE_SIZE and 0 <= ny < MAZE_SIZE:
                    neighbor = grid[ny][nx]
                    if neighbor not in visited:
                        visited.add(neighbor)
                        predecessors[neighbor] = current
                        queue.append(neighbor)

    # Reconstruct the path from exit to start
    path = []
    current = exit_cell
    while current != start_cell:
        path.append(current)
        current = predecessors.get(current)
        if current is None:
            # No path found
            return []
    path.append(start_cell)
    path.reverse()
    return path

def draw_maze(surface, grid, start_cell, exit_cell, path=[]):
    """
    Draws the entire maze on the given surface.
    Optionally, highlights the solution path if provided.
    """
    for row in grid:
        for cell in row:
            # Determine cell color
            if cell == start_cell:
                color = RED
            elif cell == exit_cell:
                color = BLUE
            elif cell in path:
                color = GREEN
            else:
                color = BLACK
            cell.draw(surface, color)

    pygame.display.flip()

def animate_path(surface, path, delay=100):
    """
    Animates the traversal of the given path by sequentially coloring the cells in green.
    """
    for cell in path:
        for event in pygame.event.get():
            if event.type == pygame.QUIT:
                pygame.quit()
                sys.exit()

        cell.draw(surface, GREEN)
        pygame.display.flip()
        pygame.time.delay(delay)  # Delay in milliseconds

def create_grid():
    """Creates and returns a grid of cells representing the maze."""
    return [[Cell(x, y) for x in range(MAZE_SIZE)] for y in range(MAZE_SIZE)]

def main():
    clock = pygame.time.Clock()
    grid = create_grid()

    # Generate the maze
    start_cell, exit_cell = set_start_and_exit(grid)
    generate_maze(grid, start_cell)

    # Find the optimal path
    path = find_optimal_path(grid, start_cell, exit_cell)
    if not path:
        print("No path found between start and exit.")
        pygame.quit()
        sys.exit()

    # Initial draw of the maze before animation
    draw_maze(WINDOW, grid, start_cell, exit_cell, path=[])

    # Animate the path traversal
    animate_path(WINDOW, path, delay=100)

    # Final draw to ensure the entire path is highlighted
    draw_maze(WINDOW, grid, start_cell, exit_cell, path=path)

    # Main loop to keep the window open
    running = True
    while running:
        clock.tick(30)  # Limit FPS to 30
        for event in pygame.event.get():
            if event.type == pygame.QUIT:
                running = False

    pygame.quit()
    sys.exit()

if __name__ == "__main__":
    main()