Speeding Up Dynamic Shortest-Path Algorithms

Authors:
Luciana S. Buriol;Mauricio G. C. Resende;Mikkel Thorup
Affiliations:
Instituto de Informática, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul 91501, Brazil;Algorithms and Optimization Research Department, AT&T Labs Research, Florham Park, New Jersey 07932;Algorithms and Optimization Research Department, AT&T Labs Research, Florham Park, New Jersey 07932
Venue:
INFORMS Journal on Computing
Year:
2008

Citing 0
Cited 5

Batch Dynamic Single-Source Shortest-Path Algorithms: An Experimental Study

SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
Fast paths in large-scale dynamic road networks

Computational Optimization and Applications
Maintaining dynamic minimum spanning trees: An experimental study

Discrete Applied Mathematics
GRACE: a generational randomized ACO for the multi-objective shortest path problem

EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Bidirectional A* search on time-dependent road networks

Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Dynamic shortest-path algorithms update the shortest paths taking into account a change in an arc weight. This paper describes a new generic technique that allows the reduction of heap sizes used by several dynamic single-destination shortest-path algorithms. For unit weight changes, the updates can be done without heaps. These reductions almost always reduce the computational times for these algorithms. In computational testing, several dynamic shortest-path algorithms with and without the heap-reduction technique are compared. Speedups of up to a factor of 1.8 were observed using the heap-reduction technique on random weight changes and of over a factor of five on unit weight changes. We compare as well with Dijkstra's algorithm, which recomputes the paths from scratch. With respect to Dijkstra's algorithm, speedups of up to five orders of magnitude are observed.