DRIVING TABU SEARCH WITH CASE-BASED REASONING

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 op timisation problem for that they really produce high-quality solutions . This non-trivial task is most commonly left to the competent user. I n this paper, we investigate the use of an AI technique for configurin g a basic meta-heuristic without any user interaction. In this aim, we introduce a Case-Based Reasoning approach to automatically perform in tensification-like control of operator selection in Tabu Search. Cases capture search experience concerning operator selection related to th e particular state description. They are reused to improve the selecti on of operators that apply in similar slates. The proposed method is d omain independent; it integrates a first-order representation language for problem modelling. Experimental evaluation on uncapacitated and c apacitated facility location benchmark problems is provided. (C) 1997 Published by Elsevier Science B.V.