Hybrid metaheuristic algorithms for minimum weight dominating set

2013 ◽  
Vol 13 (1) ◽  
pp. 76-88 ◽  
Author(s):  
Anupama Potluri ◽  
Alok Singh
Keyword(s):  
Dominating Set ◽  
Minimum Weight ◽  
Metaheuristic Algorithms ◽  
Hybrid Metaheuristic

DYNAMIC PROGRAMMING ON INTERVALS

1993 ◽  
Vol 03 (03) ◽  
pp. 323-330 ◽  
Author(s):  
TAKAO ASANO
Keyword(s):  
Dynamic Programming ◽  
Dominating Set ◽  
Minimum Weight ◽  
Time Algorithm ◽  
Interval Graph ◽  
Efficient Implementation ◽  
Maximum Weight ◽  
Circular Arc ◽  
Maximum Weight Clique

We consider problems on intervals which can be solved by dynamic programming. Specifically, we give an efficient implementation of dynamic programming on intervals. As an application, an optimal sequential partition of a graph G=(V, E) can be obtained in O(m log n) time, where n=|V| and m=|E|. We also present an O(n log n) time algorithm for finding a minimum weight dominating set of an interval graph G=(V, E), and an O(m log n) time algorithm for finding a maximum weight clique of a circular-arc graph G=(V, E), provided their intersection models of n intervals (arcs) are given.

scholarly journals Local Search for Minimum Weight Dominating Set with Two-Level Configuration Checking and Frequency Based Scoring Function (Extended Abstract)

2017 ◽  
Author(s):  
Yiyuan Wang ◽  
Shaowei Cai ◽  
Minghao Yin
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Dominating Set ◽  
Minimum Weight ◽  
Scoring Function ◽  
Solution Quality ◽  
Configuration Checking ◽  
New Variant ◽  
New Ideas ◽  
Better Than

The Minimum Weight Dominating Set (MWDS) problem is an important generalization of the Minimum Dominating Set (MDS) problem with extensive applications. This paper proposes a new local search algorithm for the MWDS problem, which is based on two new ideas. The first idea is a heuristic called two-level configuration checking (CC2), which is a new variant of a recent powerful configuration checking strategy (CC) for effectively avoiding the recent search paths. The second idea is a novel scoring function based on the frequency of being uncovered of vertices. Our algorithm is called CC2FS, according to the names of the two ideas. The experimental results show that, CC2FS performs much better than some state-of-the-art algorithms in terms of solution quality on a broad range of MWDS benchmarks.

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