Machine Learning-Enhanced Tabu Search for Tactical Wireless Network Design
Quick summary
arXiv:2608.28627v1 Announce Type: new Abstract: Designing high-performance tactical wireless networks under realistic operational constraints gives rise to challenging combinatorial optimization problems, where the evaluation of candidate solutions relies on detailed physical and traffic-aware models. Although classical metaheuristics such as Tabu Search offer effective mechanisms for exploring large search spaces, their computational cost remains high because numerous candidate moves must be evaluated at every iteration. In this paper, we propose a data-driven framework that improves the effi
Key takeaways
- arXiv:2608.28627v1 Announce Type: new Abstract: Designing high-performance tactical wireless networks under realistic operational constraints gives rise to challenging combinatorial optimization problems, where the evaluation of candidate solutions relies on detailed physical and traffic-aware models.
- Although classical metaheuristics such as Tabu Search offer effective mechanisms for exploring large search spaces, their computational cost remains high because numerous candidate moves must be evaluated at every iteration.
- In this paper, we propose a data-driven framework that improves the effi
Why it matters
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