Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6856557 | Information Sciences | 2018 | 26 Pages |
Abstract
The maximum min-sum dispersion problem (Max-Minsum DP) is an important representative of a large class of dispersion problems. Having numerous applications in practice, the NP-hard Max-Minsum DP is however computationally challenging. This paper introduces an effective solution-based tabu search (SBTS) algorithm for solving the Max-Minsum DP approximately. SBTS is characterized by the joint use of hash functions to determine the tabu status of candidate solutions and a parametric constrained swap neighborhood to enhance computational efficiency. Experimental results on 140 benchmark instances commonly used in the literature demonstrate that the proposed algorithm competes favorably with the state-of-the-art algorithms both in terms of solution quality and computational efficiency. In particular, SBTS improves the best-known results for 80 out of the 140 instances, while matching 51 other best-known solutions. We conduct a computational analysis to identify the respective roles of the hash functions and the parametric constrained swap neighborhood.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Xiangjing Lai, Dong Yue, Jin-Kao Hao, Fred Glover,