Abstract
Multiobjective search is a generalization of the Shortest Path Problem where several (usually conflicting) criteria are optimized simultaneously. The paper presents an extension of the single-objective IDA* search algorithm to the multiobjective case. The new algorithm is illustrated with an example, and formal proofs are presented on its termination, completeness, and admissibility. The algorithm is evaluated over a set of random tree search problems, and is found to be more efficient than IDMOA*, a previous extension of IDA* to the multiobjective case. © Springer-Verlag 2009.
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CITATION STYLE
Coego, J., Mandow, L., & Pérez De La Cruz, J. L. (2009). A new approach to iterative deepening multiobjective A*. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5883 LNAI, pp. 264–273). https://doi.org/10.1007/978-3-642-10291-2_27
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