def heuristic_multi_goal(curr_child, goal_list, path_cost, temp_cost, closed_list, heu_val):
dist_2goal = np.zeros(len(goal_list))
i = 0
for goal in goal_list:
dist_2goal[i] = util.heuristicFcn(curr_child, goal)
i += 1
min_dist_2goal = np.amin(dist_2goal)
list = [curr_child]
list.extend(goal_list)
tree = MST.maze2tree(goal_list)
min_tree = minimum_spanning_tree(tree, overwrite=False)
min_tree = min_tree.toarray().astype(int)
MSTweight = MST.findMSTweight(min_tree)
heuristic = MSTweight + min_dist_2goal + temp_cost
if (closed_list == 0) or (heuristic < heu_val):
hn = heuristic
cost = path_cost + 1
flag = 1
else:
hn = heu_val
cost = 0
flag = 0
return flag, hn, cost
