HEA-D: A Hybrid Evolutionary Algorithm for Diversified Top-k Weight Clique Search Problem

Abstract

The diversified top-k weight clique (DTKWC) search problem is an important generalization of the diversified top-k clique (DTKC) search problem with extensive applications, which extends the DTKC search problem by taking into account the weight of vertices. In this paper, we formulate the DTKWC search problem using mixed integer linear program constraints and propose an efficient hybrid evolutionary algorithm (HEA-D) which combines a clique-based crossover operator and an effective simulated annealing-based local optimization procedure to find high-quality local optima. The experimental results show that HEA-D performs much better than the existing methods on two representative real-world benchmarks.