Approximate bi-criteria search by efficient representation of subsets of the Pareto-optimal frontier

TL;DR

This paper introduces an efficient approximation method for the bi-criteria shortest-path problem that significantly speeds up computation by pruning solutions, making it practical for large graphs.

Contribution

The authors propose a novel approximation approach that uses pairs of paths to efficiently estimate the Pareto frontier, outperforming existing exact algorithms on large instances.

Findings

Achieves over 8.5x average speedup on large roadmaps.

Maximal speedup exceeds 148x compared to state-of-the-art.

Effectively prunes solutions to handle large-scale bi-criteria problems.

Abstract

We consider the bi-criteria shortest-path problem where we want to compute shortest paths on a graph that simultaneously balance two cost functions. While this problem has numerous applications, there is usually no path minimizing both cost functions simultaneously. Thus, we typically consider the set of paths where no path is strictly better then the others in both cost functions, a set called the Pareto-optimal frontier. Unfortunately, the size of this set may be exponential in the number of graph vertices and the general problem is NP-hard. While existing schemes to approximate this set exist, they may be slower than exact approaches when applied to relatively small instances and running them on graphs with even a moderate number of nodes is often impractical. The crux of the problem lies in how to efficiently approximate the Pareto-optimal frontier. Our key insight is that the Pareto-optimal frontier can be approximated using pairs of paths. This simple observation allows us to run a best-first-search while efficiently and effectively pruning away intermediate solutions in order to obtain an approximation of the Pareto frontier for any given approximation factor. We compared our approach with an adaptation of BOA*, the state-of-the-art algorithm for computing exact solutions to the bi-criteria shortest-path problem. Our experiments show that as the problem becomes harder, the speedup obtained becomes more pronounced. Specifically, on large roadmaps, we obtain an average speedup of more than $\times 8.5$ and a maximal speedup of over $\times 148$.