Abstract
The goal of this paper is twofold. First, we introduce a class of local search procedures for solving optimization and constraint problems. These procedures are based on various heuristics for choosing variables and values in order to examine a general neighborhood. Second, four combinations of heuristics are empirically evaluated by using the graph-coloring problem and a real world application — the frequency assignment problem. The results are also compared with those obtained with other approaches including simulated annealing, Tabu search, constraint programming and heuristic graph coloring algorithms. Empirical evidence shows the benefits of this class of local search procedures for solving large and hard instances.
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Work partially supported by the CNET (French National Research Center for Telecommunications) under the grant No.940B006-01.
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Hao, JK., Dorne, R. (1996). Empirical studies of heuristic local search for constraint solving. In: Freuder, E.C. (eds) Principles and Practice of Constraint Programming — CP96. CP 1996. Lecture Notes in Computer Science, vol 1118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61551-2_75
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DOI: https://doi.org/10.1007/3-540-61551-2_75
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