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An Evaluation of Best Compromise Search in Graphs

Abstract : This work evaluates two different approaches for multicriteria graph search problems using compromise preferences. This approach focuses search on a single solution that represents a balanced tradeoff between objectives, rather than on the whole set of Pareto optimal solutions. We review the main concepts underlying compromise preferences, and two main approaches proposed for their solution in heuristic graph problems: naive Pareto search (NAMOA*), and a k-shortest-path approach (kA*). The performance of both approaches is evaluated on sets of standard bicriterion road map problems. The experiments reveal that the k-shortest-path approach looses effectiveness in favor of naive Pareto search as graph size increases. The reasons for this behavior are analyzed and discussed.
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https://hal.archives-ouvertes.fr/hal-01368804
Contributor : Christine Okret-Manville <>
Submitted on : Tuesday, September 20, 2016 - 9:28:40 AM
Last modification on : Thursday, January 21, 2021 - 11:38:01 AM

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Enrique Machuca, Lawrence Mandow, Lucie Galand. An Evaluation of Best Compromise Search in Graphs. 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Sep 2013, Madrid, Spain. pp.1-11, ⟨10.1007/978-3-642-40643-0_1⟩. ⟨hal-01368804⟩

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