Simulated annealing with stochastic local search for minimum dominating set problem

2011 ◽  
Vol 3 (2) ◽  
pp. 97-109 ◽  
Author(s):  
Abdel-Rahman Hedar ◽  
Rashad Ismail
Keyword(s):  
Simulated Annealing ◽  
Local Search ◽  
Dominating Set ◽  
Stochastic Local Search ◽  
Minimum Dominating Set ◽  
Dominating Set Problem

scholarly journals Two-goal Local Search and Inference Rules for Minimum Dominating Set

2020 ◽  
Author(s):  
Shaowei Cai ◽  
Wenying Hou ◽  
Yiyuan Wang ◽  
Chuan Luo ◽  
Qingwei Lin
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Dominating Set ◽  
Inference Rules ◽  
Local Search Algorithm ◽  
Minimum Dominating Set ◽  
Massive Graphs ◽  
Construction Procedure ◽  
Almost All ◽  
Main Ideas

Minimum dominating set (MinDS) is a canonical NP-hard combinatorial optimization problem with applications. For large and hard instances one must resort to heuristic approaches to obtain good solutions within reasonable time. This paper develops an efficient local search algorithm for MinDS, which has two main ideas. The first one is a novel local search framework, while the second is a construction procedure with inference rules. Our algorithm named FastDS is evaluated on 4 standard benchmarks and 3 massive graphs benchmarks. FastDS obtains the best performance for almost all benchmarks, and obtains better solutions than state-of-the-art algorithms on massive graphs.

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