Speeding Up Dominance Checks in Multi-Objective Search: New Techniques and Data Structures

Han Zhang, Oren Salzman, Ariel Felner, T. K. Satish Kumar, Carlos Hernández Ulloa, Sven Koenig

Research output: Contribution to journalConference articlepeer-review


In multi-objective search, given a directed graph where each edge is annotated with multiple cost metrics, a start state, and a goal state, we are interested in computing the Pareto frontier, i.e., the set of all undominated paths from the start state to the goal state. Almost all multi-objective search algorithms use dominance checks to determine if a search node can be pruned. Since dominance checks are performed in the inner loop of the multi-objective search, they are the most timeconsuming part of it. In this paper, we propose (1) two novel techniques to reduce duplicate dominance checks and (2) a simple data structure that enables more efficient dominance checks. Our experimental results show that combining our proposed techniques and data structure speeds up LTMOA*, a state-of-the-art multi-objective search algorithm, by up to an order of magnitude on road network instances.

Original languageEnglish
Pages (from-to)228-232
Number of pages5
JournalThe International Symposium on Combinatorial Search
Issue number1
StatePublished - 2024
Externally publishedYes
Event17th International Symposium on Combinatorial Search, SoCS 2024 - Kananaskis, Canada
Duration: 6 Jun 20248 Jun 2024

Bibliographical note

Publisher Copyright:
© 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.


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