Restart and Random Walk in Local Search for Maximum Vertex Weight
Cliques with Evaluations in Clustering Aggregation
Abstract
The Maximum Vertex Weight Clique (MVWC)
problem is NP-hard and also important in realworld applications. In this paper we propose to use
the restart and the random walk strategies to improve local search for MVWC. If a solution is revisited in some particular situation, the search will
restart. In addition, when the local search has no
other options except dropping vertices, it will use
random walk. Experimental results show that our
solver outperforms state-of-the-art solvers in DIMACS and finds a new best-known solution. Moreover it is the unique solver which is comparable
with state-of-the-art methods on both BHOSLIB
and large crafted graphs. Also it achieves 100%
success rates over all the winner determination
graphs within 500s. Furthermore we evaluated our
solver in clustering aggregation. Experimental results on a number of real data sets demonstrate that
our solver outperforms the state-of-the-art for solving the derived MVWC problem and helps improve
the final clustering results