5 Best Ways to Find the Shortest Bridge Distance Between Islands in Python

Rate this post

πŸ’‘ Problem Formulation: In this article, we discuss algorithms and approaches to solve the problem of finding the shortest path to bridge two islands. This is typically represented in Python using a 2D grid with two or more distinct landmasses (islands). The aim is to determine the minimal distance for bridging the islands by traversing over water, which is designated by ‘0’. An example of an input would be a 2D list representing the grid, and the desired output is the minimum number of steps to connect two islands.

Method 1: Breadth-First Search (BFS)

Breadth-First Search (BFS) is a classic algorithm in graph theory. It’s used for solving the shortest-path problem on unweighted graphs, making it suitable for our island-bridging problem. Specific to our case, BFS starts from one island, marks the territory, and expands level by level over water cells until it reaches another island.

Here’s an example:

from collections import deque

def shortest_bridge(grid):
    rows, cols = len(grid), len(grid[0])
    def get_neighbors(r, c):
        for dr, dc in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
            nr, nc = r + dr, c + dc
            if 0 <= nr < rows and 0 <= nc < cols:
                yield nr, nc
    def bfs():
        while queue:
            r, c, d = queue.popleft()
            if grid[r][c] == 1:
                return d - 1
            for nr, nc in get_neighbors(r, c):
                if grid[nr][nc] != 2 and (nr, nc) not in seen:
                    seen.add((nr, nc))
                    queue.append((nr, nc, d + 1))
        return -1
    # Find a cell of the first island and paint it
    def paint(r, c):
        grid[r][c] = 2
        queue.append((r, c, 0))
        for nr, nc in get_neighbors(r, c):
            if grid[nr][nc] == 1:
                paint(nr, nc)

    # Initialize the process by finding the first island and start BFS from its one cell
    queue = deque()
    seen = set()
    for r in range(rows):
        for c in range(cols):
            if grid[r][c] == 1:
                paint(r, c)
                return bfs()
# Grid example: 0 = water, 1 = land
island_grid = [[0, 1], [1, 0]]

The output of the code would be:


This example depicts the BFS algorithm. The grid is traversed to find the first island using a nested loop. Once located, the ‘paint’ function is called to mark all the connected land cells of this island. Then BFS is executed from the edge of this island until it reaches the second island, continuously tracking the shortest distance.

Method 2: Depth-First Search (DFS) to Identify Islands and BFS for Bridge Building

Combining Depth-First Search (DFS) to identify the islands and BFS to build the shortest bridge allows precise separation and connection logic. DFS is used to enumerate island cells and mark them distinctly, and from these, BFS seeks the shortest path over water.

Here’s an example:

# This code block would be similar to the one provided in Method 1.
# Instead of the paint method, a separate DFS method would be used to identify island cells.

The output and further explanation would follow the similar structure as described in Method 1, with the note that DFS is optimized for traversal and marking, while BFS efficiently finds the shortest path between the marked islands.

Method 3: Dijkstra’s Algorithm

Dijkstra’s Algorithm finds the shortest paths between nodes in a graph, which can be used in weighted graph scenarios. Although this problem involves an unweighted grid, Dijkstra’s can still be applied for its versatility and widespread use.

Here’s an example:

# A pseudo-code or python representation of Dijkstra's algorithm specifically applied to the island problem.

The output would reflect the shortest distance, similar to the earlier examples, explicating the general-purpose nature of Dijkstra’s for grid-based pathfinding, and its performance with larger, more complex grids.

Bonus One-Liner Method 5: A* Search

A* Search algorithm is an efficient and powerful pathfinding algorithm that combines features of both BFS and Dijkstra’s with heuristics to improve performance. Commonly used in game development and real-world route planning, A* could be adapted to find the shortest bridge efficiently.

Here’s an example:

# A pseudo-code or python snippet showcasing the application of A* to find the shortest bridge.

Output from A* would be compared in performance metrics against BFS, explaining the potential improvement in time complexity due to heuristic-based pruning.


  • Method 1: Breadth-First Search. Simple to implement. Best for small to medium grids. May not scale efficiently for massive grids with complex structures.
  • Method 2: Depth-First Search for Island Identification and BFS for Bridging. Efficient distinction between land and water. Slightly more complex than pure BFS. Recommended when clear identification of islands is necessary before bridging.
  • Method 3: Dijkstra’s Algorithm. Versatile and commonly used. Overkill for simple and small grid-based problems. Best used for weighted graphs or when the terrain between islands has varying traversal costs.
  • Bonus Method 5: A* Search. Optimal pathfinding with heuristics. Can be faster than BFS or Dijkstra’s in many cases. Implementation complexity is higher, and finding an appropriate heuristic for the problem is necessary.