The shortest path problem in graphs is a fundamental optimization problem which has stimulated research for several decades. Numerous real-world applications are modeled as graphs and shortest path computation is a frequent operation performed on them. Many graphs happen to be very large like road networks or routing networks. Shortest path computation on them is a challenge because of the low performance due to its large nature. Already existing graph algorithms are not suitable for large graphs.In this paper, an attempt is made to solve the problem of finding an efficient point-to-point shortest path algorithm for graphs of larger sizes. First run the A * algorithm with binary heap implementation from both the directions. The nodes extracted from both directions are saved and then genetic algorithm is used to find the shortest path. The bi-directional strategy reduces the search space and the genetic algorithm optimizes the search problem to give best result. The final results illustrates that this novel approach with the optimization strategies achieves high scalability and performance.
Computing shortest paths in graphs is one of the most fundamental and well-studied problems in combinatorial optimization. Numerous real-world applications have stimulated research investigations in this area. Several applications include large graphs involving thousands of nodes, which we cannot assume to be fully loaded into memory. The problem is of much interest, when the nodes and edges have several constraints to be satisfied apart from being large, in the computation of shortest path. Conventional Dijkstra's algorithm does not serve the purpose. There has not been much research done in this area, although some papers investigate the problem of large graphs.The problem of finding an efficient point-to-point shortest path algorithm for graphs of larger sizes, satisfying node as well as link constraints is solved using two optimization strategies. First, we implement bi-directional Dijkstra's algorithm with priority queue implementation using the heuristic value, in the path finding. The bi-directional strategy reduces the search space. Second, we introduce index of the graph table to preserve the local shortest segments, and exploit the table to further improve the performance. The final experimental results illustrates that this novel approach with the optimization strategies achieves high scalability and performance.
General TermsGraph Theory, Algorithms Keywords Bidirectional Dijkstra's algorithm; Point-to-point shortest path algorithm satisfying node and link constraints; Combinatorial Optimization
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