Driving Tabu search with case-based reasoning
Résumé
When it is important to solve hard optimisation problems efficiently, e.g. as in Decision Support Systems, meta-heuristics like Tabu Search often propose valuable alternatives in case exact optimisation is not available. Further, such techniques are in general flexible enough to adapt problem modelling according to end user feed-back. However, meta-heuristics need to be tailored to each particular modelling of the optimisation problem for that they really produce high-quality solutions. This non-trivial task is most commonly left to the competent user. In this paper, we investigate the use of an AI technique for configuring a basic meta-heuristic without any user interaction. In this aim, we introduce a Case-Based Reasoning approach to automatically perform intensification-like control of operator selection in Tabu Search. Cases capture search experience concerning operator selection related to the particular state description. They are reused to improve the selection of operators that apply in similar states. The proposed method is domain independent; it integrates a first-order representation language for problem modelling. Experimental evaluation on uncapacitated and capacitated facility location benchmark problems is provided.