Analysis and Performance Evaluation of Various Shortest Path Algorithms

Extensive study has been conducted on the problem of finding the shortest path in a graph, leading to the creation of several approaches. The effectiveness of the following algorithms in finding the shortest path is compared and contrasted in this study: Iterative Deepening A* (IDA*), Uniform Cost Search (UCS), Breadth-First Search (BFS), Depth-First Search (DFS), Dijkstra’s, and Bellman-Ford. Due to its consideration of more complex data structures and heuristics, the A* algorithm performs better than the Greedy method. Dijkstra’s approach may be used to find the shortest route in weighted graphs in an optimum and efficient manner. The Bellman-Ford approach, which handles negative edge weights, is also taken into account in the study. To find the shortest paths, we explore the potential of the graph traversal algorithms BFS and DFS. Whereas IDA* efficiently determines the optimal solution by fusing DFS with heuristic approximations, UCS expands Dijkstra’s method to incorporate edge cost information. By comparing them, the study aims to provide insight into the capabilities and suitability of these algorithms in different scenarios. The best approach depends on several factors, including the graph’s characteristics, the presence of negative weights, and the availability of heuristic information. Every approach has benefits and drawbacks that determine which use cases are best suited for it. In summary, a variety of techniques for locating the shortest path in a graph are offered by the IDA*, BFS, DFS, Bellman-Ford, Dijkstra, and A* algorithms. When selecting a technique, it is important to take into account the unique requirements and constraints of the present problem.