Intelligent Neighborhood Exploration in Local Search Heuristics

Standard tabu search methods are based on the complete exploration of current solution neighborhood. However, for some problems with very large neighborhood or time-consuming evaluation, the total exploration of the neighborhood is impractical. In this paper, we present an adaptive exploration of neighborhood using extension and restriction mechanisms represented by a loop detection mechanism and a tabu list structure. This approach is applied to the K-coloring problem and evaluated on standard benchmarks like DIMACS in comparison with more powerful recently published algorithms