Abstract:This paper reports a Fast Local Search (FLS) algorithm which helps to improve the efficiency of hill climbing and a Guided Local Search (GLS) Algorithm which is developed to help local search to escape local optima and distribute search effort. To illustrate how these algorithms work, this paper describes their application to British Telecom's workforce scheduling problem, which is a hard real life problem. The effectiveness of FLS and GLS are demonstrated by the fact that they both out-perform all the methods applied to this problem so far, which include simulated annealing, genetic algorithms and constraint logic programming.
Guided local search (GLS) is a metaheuristic method proposed to solve combinatorial optimization problems. It is a high‐level strategy that applies an efficient penalty‐based approach to interact with the local improvement procedure. This interaction creates a process capable of escaping from local minima, which improves the efficiency and robustness of the underlying local search algorithms. Fast local search (FLS) is a local search algorithm, which improves the efficiency of local search by reducing the size of the neighborhood. GLS can be efficiently combined with FLS in the form of guided fast local search (GFLS). This article describes the principles of GLS, FLS, and GFLS. It also surveys GLS's extensions, hybrids, and applications.
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