TL;DR
This paper introduces a parallel external-memory framework for bidirectional heuristic search, specifically developing PEM-BAE*, which outperforms existing unidirectional and bidirectional algorithms on large-scale problems.
Contribution
It presents the first PEM framework for bidirectional heuristic search and demonstrates the superior performance of PEM-BAE* over other algorithms on hard problems.
Findings
PEM-BAE* outperforms PEM-A* and PEM-MM algorithms.
Bidirectional search algorithms outperform unidirectional ones.
The framework enables scaling to larger, more complex problems.
Abstract
Parallelization and External Memory (PEM) techniques have significantly enhanced the capabilities of search algorithms when solving large-scale problems. Previous research on PEM has primarily centered on unidirectional algorithms, with only one publication on bidirectional PEM that focuses on the meet-in-the-middle (MM) algorithm. Building upon this foundation, this paper presents a framework that integrates both uni- and bi-directional best-first search algorithms into this framework. We then develop a PEM variant of the state-of-the-art bidirectional heuristic search (BiHS) algorithm BAE* (PEM-BAE*). As previous work on BiHS did not focus on scaling problem sizes, this work enables us to evaluate bidirectional algorithms on hard problems. Empirical evaluation shows that PEM-BAE* outperforms the PEM variants of A* and the MM algorithm, as well as a parallel variant of IDA*. These…
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