Multi-objective Search via Lazy and Efficient Dominance Checks

Carlos Hernández, William Yeoh, Jorge A. Baier, Ariel Felner, Oren Salzman, Han Zhang, Shao Hung Chan, Sven Koenig

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Multi-objective search can be used to model many real-world problems that require finding Pareto-optimal paths from a specified start state to a specified goal state, while considering different cost metrics such as distance, time, and fuel. The performance of multi-objective search can be improved by making dominance checking-an operation necessary to determine whether or not a path dominates another-more efficient. This was shown in practice by BOA*, a state-of-the-art bi-objective search algorithm, which outperforms previously existing bi-objective search algorithms in part because it adopts a lazy approach towards dominance checking. EMOA*, a recent multi-objective search algorithm, generalizes BOA* to more-than-two objectives using AVL trees for dominance checking. In this paper, we first propose Linear-Time Multi-Objective A* (LTMOA*), a multi-objective search algorithm that implements more efficient dominance checking than EMOA* using simple data structures like arrays. We then propose LazyLTMOA*, which employs a lazier approach by removing dominance checking during node generation. Our experimental results show that LazyLTMOA* outperforms EMOA* by up to an order of magnitude in terms of runtime.

Original languageEnglish
Title of host publicationProceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
EditorsEdith Elkind
Number of pages8
ISBN (Electronic)9781956792034
StatePublished - 1 Jan 2023
Event32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 - Macao, China
Duration: 19 Aug 202325 Aug 2023

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence


Conference32nd International Joint Conference on Artificial Intelligence, IJCAI 2023

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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