点击次数：

论文类型：期刊论文

发表时间：2014-10-01

发表刊物：JOURNAL OF HEURISTICS

收录刊物：SCIE、EI、Scopus

卷号：20

期号：5

页面范围：589-615

ISSN号：1381-1231

关键字：Local search; p-median; Lagrangian relaxation; Lin-Kernighan neighborhood

摘要：Local search algorithms play an essential role in solving large-scale combinatorial optimization problems. Traditionally, the local search procedure is guided mainly by the objective function of the problem. Hence, the greedy improvement paradigm poses the potential threat of prematurely getting trapped in low quality attraction basins. In this study, we intend to utilize the information extracted from the relaxed problem, to enhance the performance of the local search process. Considering the Lin-Kernighan-based local search (LK-search) for the p-median problem as a case study, we propose the Lagrangian relaxation Assisted Neighborhood Search (LANS). In the proposed algorithm, two new mechanisms, namely the neighborhood reduction and the redundancy detection, are developed. The two mechanisms exploit the information gathered from the relaxed problem, to avoid the search from prematurely targeting low quality directions, and to cut off the non-promising searching procedure, respectively. Extensive numerical results over the benchmark instances demonstrate that LANS performs favorably to LK-search, which is among the state-of-the-art local search algorithms for the p-median problem. Furthermore, by embedding LANS into other heuristics, the best known upper bounds over several benchmark instances could be updated. Besides, run-time distribution analysis is also employed to investigate the reason why LANS works. The findings of this study confirm that the idea of improving local search by leveraging the information induced from relaxed problem is feasible and practical, and might be generalized to a broad class of combinatorial optimization problems.