Guided local search joins the elite in discrete optimisation
Christos Voudouris, Edward P. K. Tsang · DIMACS series in discrete mathematics and theoretical computer science · 2001
Developed from constraint satisfaction as well as operations research ideas, Guided Local Search (GLS) and Fast Local Search are novel meta-heuristic search methods for constraint satisfaction and optimisation. GLS sits on top of other local-search algorithms. The basic principle of GLS is to penalise features exhibited by the candidate solution when a localsearch algorithm settles in a local optimum. Using penalties is an idea used in operations research before. The novelty in GLS is in the way that features are selected and penalised. FLS is a way of reducing the size of the neighbourhood. GLS and FLS together have been applied to a non-trivial number of satisfiability and optimisation problems and achieved remarkable result. One of their most outstanding achievements is in the well-studied travelling salesman problem, in which they obtained results as good as, if not better than the state-of-the-art algorithms. In this paper, we shall outline these algorithms and describe some of their discrete optimisation applications.