An Evaluation of Best Compromise Search in Graphs

Abstract

LNCS n°8109International audienceThis 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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“…This was the solution whose Euclidean distance from the ideal point of Utopia was the smallest among all analyzed points of the set. Such a solution had coordinates x 1comp = 0.0750 g/mL, x 2comp = 9.0000 min and x 3comp = 214.2857 W. At this stage, it should be emphasized that Pareto sets are particularly extremely useful in understanding the trade-off relationships between different objectives and criteria in a multi-objective problem [77,78].…”