def random_restart(self):
configuration = [['#' for _ in range(n)] for _ in range(n)]
positions = []
i = 0
while i < n:
x, y = randint(0, n - 1), randint(0, n - 1)
if (x, y) not in positions:
positions.append((x, y))
i += 1
for i, j in positions:
configuration[i][j] = 'Q'
board = ChessboardState(configuration)
while any(board.__eq__(e) for e in self.prev_expanded_states):
configuration = [['#' for _ in range(n)] for _ in range(n)]
positions.clear()
i = 0
while i < n:
x, y = randint(0, n - 1), randint(0, n - 1)
if (x, y) not in positions:
positions.append((x, y))
i += 1
for i, j in positions:
configuration[i][j] = 'Q'
board = ChessboardState(configuration)
return ChessboardStateNode(board)
def solve(self, initial_chessboard_state):
# Structure: each column should contain only one queen
prev_expanded = []
start_time = time.time()
current_state, positions = self.get_chessboard_and_positions(
initial_chessboard_state)
while True:
print("Current state #attacks =",
current_state.get_attacking_count())
current_state.print_chessboard()
last_cost = current_state.get_attacking_count()
if last_cost == 0:
self.final_sol = current_state
break
positions = self.heuristic_move(positions)
current_state = ChessboardState(queen_positions=positions)
# while any(current_state.__eq__(x) for x in prev_expanded):
# positions = self.heuristic_move(positions)
# current_state = ChessboardState(queen_positions=positions)
self.expanded_count += 1
prev_expanded.append(current_state)
if last_cost == current_state.get_attacking_count(
): # local optima
print("--- Local Optima ---")
positions = self.random_move(positions)
current_state = ChessboardState(queen_positions=positions)
while any(current_state.__eq__(x) for x in prev_expanded):
positions = self.random_move(positions)
current_state = ChessboardState(queen_positions=positions)
self.expanded_count += 1
prev_expanded.append(current_state)
last_cost = current_state.get_attacking_count()
else:
last_cost = current_state.get_attacking_count()
self.steps_count += 1
end_time = time.time()
self.execution_time = end_time - start_time
return self.final_sol