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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