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Extending Weighted Heuristic Search to Bi-Objective Search Problems

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 6291-6298.

https://doi.org/10.24963/ijcai.2026/700

Abstract

In heuristic search, a well-known technique to speed up search while providing a suboptimality guarantee is to multiply the heuristic function by a weight w > 1. In this paper, we study the theoretical and practical implications of using such a technique in bi-objective heuristic search, a natural academic exercise that has remained unexplored. We introduce Weighted BOA∗ϵ (WBOA∗

ϵ ), a weighted version of the BOA* algorithm, which uses two real parameters: a weight w for the heuristic and an approximation factor ϵ. Higher values of w and ϵ allow for faster computation of approximate Pareto-optimal solution sets. We prove that WBOA∗ ϵ returns a representative solution set containing (w − 1, ϵ)-approximate solutions. We empirically compare it to A*pex, the state-of-the-art approximate bi-objective search algorithm. We find that WBOA∗ ϵ is competitive with A*pex: when using perfect heuristic functions, in road maps WBOA∗ϵ is faster for higher approximation factors, while in grid maps WBOA∗ϵ dominates A*pex. With imperfect heuristic functions, WBOA∗ϵ performs better than A*pex for lower approximation factors, while the opposite is true for larger approximation factors.

Hans Kühn Leiva, Jorge A. Baier, Carlos Hernandez Ulloa, Oren Salzman, Ariel Felner, and Sven Koenig. 2026. Extending Weighted Heuristic Search to Bi-Objective Search Problems. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 6291–6298. https://doi.org/10.24963/ijcai.2026/700
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