AlgorandSIMULATION v1.0
Sequential Engines(17)
Parallel Engines

A* Search Algorithm

sequentialRAM

An informed pathfinding and graph traversal algorithm that uses heuristics to find the shortest path between nodes more efficiently than Dijkstra's algorithm.

SIMULATION STEP PROGRESSStep 1 / 1
SPEED (50%)
Code Workspace
1procedure AStar(G, source, goal, h)
2 for each vertex v in G:
3 gScore[v] := infinity, fScore[v] := infinity
4 gScore[source] := 0
5 fScore[source] := h(source)
6 openSet.push(source, fScore[source])
7 while openSet is not empty do
8 current := openSet.pop_min()
9 if current == goal then
10 return path
11 end if
12 for each neighbor of current:
13 alt := gScore[current] + weight(current, neighbor)
14 if alt < gScore[neighbor] then
15 parent[neighbor] := current
16 gScore[neighbor] := alt
17 fScore[neighbor] := gScore[neighbor] + h(neighbor)
18 openSet.push(neighbor, fScore[neighbor])
19 end if
20 end for
21 end while
22end procedure
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RUNTIME COST METRICS
Comparisons0
Memory Writes0
Memory Reads0
Elements Swapped0
Computational Model:RAM (Single Core)
Theoretical Bound:O(E log V)
Space Complexity:O(V)
Communication Cost:0 (Shared Bus)
THEORETICAL ANALYSIS & RESEARCH

Conceptual Focus: An informed pathfinding and graph traversal algorithm that uses heuristics to find the shortest path between nodes more efficiently than Dijkstra's algorithm.

Real-world Industrial Applications:
  • Video game pathfinding and character navigation
  • Autonomous robotics map navigation and trajectory planning
  • GPS navigation and digital maps routing