Integrating case based reasoning and tabu search for solving optimisation problems

Abstract

Tabu search is an established heuristic optimisation technique for problems where exact algorithms are not available. It belongs to the same family as simulated annealing or genetic algorithms. It extends the basic iterative improvement scheme by adding control learning. A technique of this kind, intensification, captures experience established on a frequency-based analysis of past search. Experience is reused while the same optimisation process is going on in order to guide search to better solutions.

In this paper, we introduce a case-based reasoning approach for control learning in tabu search. Search experience concerns operator selection and is represented by cases. The aim of case reuse is to improve conflict resolution. While the proposed method is domain independent, we present its application to the NP-hard uncapacitated facility location problem. Experimental results show that adding our approach to a basic tabu search optimisation significantly improves solution quality on the evaluated benchmark problems. It reduces the gap to the optimal solution by a factor of nearly 2.