For my game, an A-star algorithm is used to find paths. It's a tad slow, though, and through profiling I find that the method that takes the most time is, out the following pseudocode, is current := the node in openset having the lowest f_score[] value. It takes up roughly 54% of runtime, which sounds like a serious problem. I implement it by making openset an std::map<coord, node> where node has a member 'f' that's the f-score. Finding the lowest f-score is naive, simply by running through the entire map and comparing each element, then returning the one with the lowest f-score. Is there a way to improve this somehow? I thought of making a map that had the f-score as the key, but that runs into the problem where two different tiles have the exact same f-score, meaning one of the tiles will be deleted. This can cause problems later.
function A*(start,goal)
closedset := the empty set // The set of nodes already evaluated.
openset := {start} // The set of tentative nodes to be evaluated, initially containing the start node
came_from := the empty map // The map of navigated nodes.
g_score[start] := 0 // Cost from start along best known path.
// Estimated total cost from start to goal through y.
f_score[start] := g_score[start] + heuristic_cost_estimate(start, goal)
while openset is not empty
current := the node in openset having the lowest f_score[] value
if current = goal
return reconstruct_path(came_from, goal)
remove current from openset
add current to closedset
for each neighbor in neighbor_nodes(current)
tentative_g_score := g_score[current] + dist_between(current,neighbor)
if neighbor in closedset and tentative_g_score >= g_score[neighbor]
continue
if neighbor not in openset or tentative_g_score < g_score[neighbor]
came_from[neighbor] := current
g_score[neighbor] := tentative_g_score
f_score[neighbor] := g_score[neighbor] + heuristic_cost_estimate(neighbor, goal)
if neighbor not in openset
add neighbor to openset
return failure
function reconstruct_path(came_from, current_node)
if current_node in came_from
p := reconstruct_path(came_from, came_from[current_node])
return (p + current_node)
else
return current_node