Yes, Dijkstra’s algorithm can be modified to terminate once the shortest path to a known destination node has been found. Although the original algorithm computes the shortest paths from a source node to all other nodes, this is not always necessary. In scenarios such as GPS navigation or single-destination network routing, it is more efficient to stop the algorithm early once the shortest path to the specific destination has been determined. This is known as an early exit optimisation. To implement this, the algorithm runs normally but includes a condition that ends the loop when the destination node is permanently labelled (i.e. its shortest distance is known). This optimisation reduces computational time and memory usage, especially in large graphs where most of the nodes are not relevant to the specific query. However, if multiple destinations are needed or the shortest paths to all nodes are required, the full algorithm should still be used.

Dijkstra’s algorithm remains efficient even on large graphs when implemented with suitable data structures. The key is to use a priority queue, commonly implemented with a binary heap or a Fibonacci heap, to manage the unvisited nodes by their tentative distances. The time complexity with a binary heap is O((V + E) * log V), where V is the number of vertices and E is the number of edges. For very large-scale graphs, like those used in nationwide logistics networks or internet infrastructure, the algorithm must be optimised to reduce computational overhead. Techniques such as graph partitioning, bidirectional search, and contraction hierarchies can improve performance. Additionally, many real-world applications use a preprocessing phase to reduce repeated calculations, storing frequently used paths in memory. By combining algorithmic improvements and efficient hardware processing, Dijkstra’s algorithm scales effectively for industrial and enterprise-level applications.

Dijkstra’s algorithm and A* are both shortest path algorithms, but they differ in approach and use cases. Dijkstra’s algorithm finds the shortest path from a source to all reachable nodes without using any knowledge about the goal’s location. This makes it ideal when the destination is unknown or when all possible paths must be explored, such as in routing tables or full-map analyses. A*, on the other hand, uses a heuristic function to estimate the distance to the goal and prioritises nodes that are likely to lead there faster. This often results in fewer nodes being explored and faster results, particularly in applications like robot pathfinding, game AI, and autonomous navigation where a specific goal is known. However, A* requires a well-defined heuristic that must be admissible (it never overestimates the true cost). Dijkstra’s algorithm is more robust and guarantees correctness without relying on a heuristic, making it a safer choice in environments with variable or unpredictable layouts.

While Dijkstra’s algorithm is widely used due to its reliability and correctness, it does have several limitations in practice. Firstly, it cannot handle negative edge weights; doing so could cause the algorithm to produce incorrect results, as it assumes once a node’s shortest path is found, it cannot be improved. This excludes its use in some financial or economic models where costs can fluctuate below zero. Secondly, for very large graphs with millions of nodes and edges, Dijkstra’s algorithm can become computationally intensive, especially without optimised data structures. Thirdly, it does not prioritise routes based on real-time preferences or constraints like time windows, vehicle capacity, or environmental factors. In these cases, more complex algorithms like A*, Bellman-Ford, or specialised metaheuristic approaches (e.g. genetic algorithms) are more suitable. Additionally, Dijkstra’s algorithm does not natively support multi-objective optimisation, which may be needed when balancing factors like time, cost, and safety.

Dijkstra’s algorithm helps improve energy efficiency by identifying routes that minimise the use of resources like fuel, battery power, or bandwidth. In transport systems, using the shortest or least-cost path reduces fuel consumption and travel time, especially for electric vehicles where efficient energy use is critical. In fleet management, the algorithm supports route optimisation that considers road grades, traffic, and charging station availability, leading to significant energy savings. In communication networks, routers using Dijkstra’s algorithm can avoid congested or high-latency links, reducing the processing power needed for retransmissions and avoiding energy waste from inefficient routing. Moreover, energy-aware routing protocols use cost metrics that include power consumption, and Dijkstra’s algorithm can incorporate these metrics into its weight system. By consistently selecting paths with minimal resource usage, the algorithm contributes to sustainable operation in both physical and digital infrastructure. Over time, this leads to reduced operational costs and a lower environmental footprint.