Combining tabu search and graph reduction to solve the maximum balanced biclique problem
This work addresses a graph optimization problem with applications in diverse domains, presenting an incremental improvement over existing methods.
The paper tackled the Maximum Balanced Biclique Problem by introducing a novel algorithm combining tabu search and graph reduction, which improved best-known results for 10 classical benchmarks and found optimal solutions for 14 real-life instances.
The Maximum Balanced Biclique Problem is a well-known graph model with relevant applications in diverse domains. This paper introduces a novel algorithm, which combines an effective constraint-based tabu search procedure and two dedicated graph reduction techniques. We verify the effectiveness of the algorithm on 30 classical random benchmark graphs and 25 very large real-life sparse graphs from the popular Koblenz Network Collection (KONECT). The results show that the algorithm improves the best-known results (new lower bounds) for 10 classical benchmarks and obtains the optimal solutions for 14 KONECT instances.