Dynamic Local Search for the Maximum Clique Problem

Details Dynamic Local Search for the Maximum Clique Problem Dynamic Local Search for the Maximum Clique Problem

2011-09-26

arxiv.org/abs/1109.5717v1

Journal Of Artificial Intelligence Research, Volume 25, pages 159-185, 2006

Computer Science

Artificial Intelligence

Scientific paper

10.1613/jair.1815

In this paper, we introduce DLS-MC, a new stochastic local search algorithm for the maximum clique problem. DLS-MC alternates between phases of iterative improvement, during which suitable vertices are added to the current clique, and plateau search, during which vertices of the current clique are swapped with vertices not contained in the current clique. The selection of vertices is solely based on vertex penalties that are dynamically adjusted during the search, and a perturbation mechanism is used to overcome search stagnation. The behaviour of DLS-MC is controlled by a single parameter, penalty delay, which controls the frequency at which vertex penalties are reduced. We show empirically that DLS-MC achieves substantial performance improvements over state-of-the-art algorithms for the maximum clique problem over a large range of the commonly used DIMACS benchmark instances.

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