Two Efficient Local Search Algorithms for Maximum Weight Clique Problem

Abstract

The Maximum Weight Clique problem (MWCP) is an important generalization of the Maximum Clique problem with wide applications. This paper introduces two heuristics and develops two local search algorithms for MWCP. Firstly, we propose a heuristic called strong configuration checking (SCC), which is a new variant of a recent powerful strategy called configuration checking (CC) for reducing cycling in local search. Based on the SCC strategy, we develop a local search algorithm named LSCC. Moreover, to improve the performance on massive graphs, we apply a low-complexity heuristic called Best from Multiple Selection (BMS) to select the swapping vertex pair quickly and effectively. The BMS heuristic is used to improve LSCC, resulting in the LSCC+BMS algorithm. Experiments show that the proposed algorithms outperform the state-of-the-art local search algorithm MN/TS and its improved version MN/TS+BMS on the standard benchmarks namely DIMACS and BHOSLIB, as well as a wide range of real world massive graphs.