The generalized independent set problem (GISP) can be conceived as a relaxation of the maximum weight independent set problem. GISP has a number of practical applications, such as forest harvesting and handling geographic uncertainty in spatial information. This work presents an iterated local search (ILS) heuristic for solving GISP. The proposed heuristic relies on two new neighborhood structures, which are explored using a variable neighborhood descent procedure. Experimental results on a well-known GISP benchmark indicate our proposal outperforms the best existing heuristic for the problem. In particular, our ILS approach was able to find all known optimal solutions and to present new improved best solutions.

广义独立集问题（GISP）可以看作是最大权重独立集问题的缓解。GISP具有许多实际应用，例如森林砍伐和处理空间信息中的地理不确定性。这项工作提出了解决GISP的迭代本地搜索（ILS）启发式方法。提议的启发式方法依赖于两个新的邻域结构，使用可变邻域下降过程对其进行了探索。在著名的GISP基准上进行的实验结果表明，我们的建议优于针对该问题的现有最佳启发式方法。特别是，我们的ILS方法能够找到所有已知的最佳解决方案，并提出新的改进的最佳解决方案。