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Population-based simulated annealing algorithm for the min-degree constrained minimum spanning tree problem
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AI
DOI：10.1016/j.swevo.2024.101791.png)
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The min-degree constrained minimum spanning tree (md-MST) problem belongs to a subset of minimum spanning tree (MST) problem variants, imposing a minimum degree constraint for each node. This problem falls within the realm of NP-hard combinatorial optimization problem. In this study, we propose a population-based simulated annealing (PSA) algorithm for the md-MST problem. Initially, a clustering-based initialization with a priority for short edges is devised to define a relatively compact yet high-quality preliminary search space. Following this, three productive neighborhood operators are designed to manage local conformation construction, partial structure refinement, and elite solution information learning. These operators offer a versatile and expansive search space, complementing the subsequent local search process, effectively optimizing the solution. Alongside a global optimal information expansion and coordinated evolution framework, PSA uniformly and flexibly explores the solution space, reaching optimal solutions eventually. Extensive experiments conducted on four widely-used datasets comprising 105 benchmark instances demonstrate that PSA outperforms state-ofthe-art approaches in terms of precision. Specifically, PSA achieves 49 current known best and 41 new best values out of 105 instances.
Keyword:
Simulated annealing
Population-based
Min-degree constrained minimum spanning
tree
Combinatorial optimization
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
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