Abstract

Guided Local Search (GLS) is an intelligent search scheme for combinatorial optimization problems. A main feature of the approach is the iterative use of local search. Information is gathered from various sources and exploited to guide local search to promising parts of the search space. The application of the method to the Travelling Salesman Problem and the Quadratic Assignment Problem is examined. Results reported show that the algorithm outperforms or compares very favorably with well-known and established optimization techniques such as simulated annealing and tabu search. Given the novelty of the approach and the very encouraging results, the method could have an important contribution to the development of intelligent search techniques for combinatorial optimization. 1. Introduction Guided Local Search is the outcome of a research project with main aim to extend the GENET neural network [29,26,5] for constraint satisfaction problems to partial constraint satisfaction [6,26] and...

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