New Front-to-Attractors Heuristic Improves Efficiency of Bidirectional Search Algorithms
Researchers have proposed a new heuristic class called front-to-attractors (F2A) designed to improve the efficiency of bidirectional search algorithms. F2A sits between existing front-to-end and front-to-front heuristics, using a small dynamic set of 'attractors' as a surrogate for the full opposite search frontier. The approach reduces pairwise evaluations by up to 11.2x compared to front-to-front methods while still achieving far fewer node expansions than simpler front-to-end methods.
Bidirectional search algorithms, which explore a problem from both start and goal simultaneously, rely heavily on heuristic functions to guide their efficiency. Existing approaches fall into two camps: front-to-end (F2E) heuristics, which estimate distance to a fixed target and are computationally cheap but less informative, and front-to-front (F2F) heuristics, which compare states across both search frontiers for greater accuracy but at high computational cost due to extensive pairwise evaluations. The newly introduced front-to-attractors (F2A) class addresses this trade-off by maintaining a small, dynamically updated set of representative 'attractor' states from the opposite frontier, rather than evaluating all frontier pairs. This design preserves much of F2F's informational richness while drastically reducing the number of pairwise computations required. Experimental results across multiple domains show F2A reduces pairwise evaluations by up to 11.2x versus F2F and achieves 4.8x fewer node expansions on average compared to F2E. Crucially, the authors report that F2A maintains the optimality guarantees associated with F2F heuristics, meaning solution quality is not sacrificed for speed. The work was submitted to arXiv in June 2026 and has not yet undergone formal peer review.
What's missing
As a preprint, this work has not been peer-reviewed. The paper does not discuss how F2A performs as problem scale increases to very large search spaces. The generalizability of the reported speedups to real-world planning applications beyond the tested benchmark domains is also not addressed.
What different sources said
- arXiv cs.AICenter
Front-to-Attractors: Modifying the Front-to-Front Heuristic in Bidirectional Search
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