Difference Between Dijkstra's Algorithm and A* Search Algorithm
Last Updated :
23 Jul, 2025
Dijkstra's Algorithm and A* Algorithm are two of the most widely used techniques. Both are employed to the find the shortest path between the nodes in a graph but they have distinct differences in their approaches and applications. Understanding these differences is crucial for the selecting the appropriate algorithm for the given problem.
The Dijkstra's Algorithm is a graph search algorithm that finds the shortest path between the starting node and all other nodes in the weighted graph. It works by exploring the all possible paths and selecting the one with the smallest cumulative weight.
Characteristics of Dijkstra's Algorithm:
- Dijkstra's Algorithm chooses the edges with minimum weight, and therefore is a Greedy Algorithm.
- Starts with the source node, assigns a tentative distance of 0 to it, and infinity to all other nodes.
- Repeatedly selects the node with the smallest tentative distance, updates the distances of its neighbors, and marks it as visited.
- When all nodes have been visited, the algorithm produces the shortest path to each node from the source.
- Dijkstra's Algorithm has O(V^2) for a graph with V vertices, but can be reduced to O((V + E) log V) using a priority queue (where E is the number of edges).
Examples:
- Network routing protocols where finding the shortest path for data transmission is critical.
- Dijkstra's Algorithm is also used to find fastest path in Telecommunication networks.
A* Algorithm is an informed search algorithm that finds the shortest path between the two nodes. It combines the benefits of the Dijkstra's Algorithm and a heuristic to the improve efficiency. A* uses a best-first search and finds the least-cost path from the given initial node to the one goal node by using the heuristic to the estimate the cost to the reach the goal.
Characteristics of A* Algorithm:
- A* Search Algorithm is a type of informed search algorithm.
- Similar to Dijkstra's, but includes a heuristic function to estimate the cost to reach the goal from each node.
- Uses a priority queue to explore nodes. The priority is determined by the sum of the cost to reach the node and the heuristic estimate of the remaining cost to reach the goal.
- Depends on the heuristic; in the worst case, it can degrade to Dijkstra's complexity, but with a good heuristic, it performs significantly better.
Difference Between Dijkstra and A* Algorithms:
The table below compares the key aspects of Dijkstra's Algorithm and A* Search Algorithm:
| Characteristics | Dijkstra's Algorithm | A* Search Algorithm |
|---|
| Algorithm Type | Greedy | Informed search (uses heuristics) |
| Initialization | Tentative distance: 0 for source, infinity for others | Tentative distance and heuristic function |
| Heuristic Use | None | Uses heuristic to estimate remaining cost to goal |
| Efficiency | Less efficient for large graphs | More efficient with a well-chosen heuristic |
| Complexity | O(V^2) or O((V + E) log V) with priority queue | Depends on heuristic; typically better than Dijkstra's |
| Use Case | Network routing protocols | Navigation systems, games, AI pathfinding |
| Strengths | Guarantees shortest path for all nodes | Efficient pathfinding with good heuristic |
| Weaknesses | Can be slow for large graphs | Performance depends on heuristic quality |
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