A New Approach to Iterative Deepening Multiobjective A*

Javier Coego, Lawrence Mandow, and J.L. Pérez de la Cruz.
A new approach to iterative deepening multiobjective a*.
In Roberto Serra and Rita Cucchiara, editors, AI *IA 2009: Emergent Perspectives in Artificial Intelligence, volume 5883 of Lecture Notes in Computer Science, pages 264-273. Springer Berlin / Heidelberg, 2009. 10.1007/978-3-642-10291-2_27.


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.


  affiliation={Universidad de M{\'a}laga Dpto. Lenguajes y Ciencias de la Computaci{\'o}n 29071 M{\'a}laga Spain},
  keyword={Computer Science},
  url={http: //},
  author={Coego, Javier and Mandow, Lawrence and P{\'e}rez de la Cruz, J.L.},
  title={A New Approach to Iterative Deepening Multiobjective A*},
  editor={Serra, Roberto and Cucchiara, Rita},
  booktitle={AI *IA 2009: Emergent Perspectives in Artificial Intelligence},
  series={Lecture Notes in Computer Science},
  publisher={Springer Berlin / Heidelberg},
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