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.
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Coego, J., Mandow, L., Pérez de la Cruz, J.L. (2009). A New Approach to Iterative Deepening Multiobjective A*. In: Serra, R., Cucchiara, R. (eds) AI*IA 2009: Emergent Perspectives in Artificial Intelligence. AI*IA 2009. Lecture Notes in Computer Science(), vol 5883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10291-2_27
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DOI: https://doi.org/10.1007/978-3-642-10291-2_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10290-5
Online ISBN: 978-3-642-10291-2
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