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Learning to Sample in Variable Neighborhood Search Algorithm for Urban Cable Routing Optimization

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arXiv:2512.19321v2 Announce Type: replace Abstract: Urban underground cable construction is essential for enhancing power grid reliability, yet the high construction costs demand systematic optimization. Constrained by road network infrastructure, this problem requires consideration of both connectivity relationships between substations and specific routing strategies along road networks, constituting a large-scale bilevel combinatorial optimization problem. Insufficient attention to routing subproblems in traditional research and simplistic algorithmic designs ill-equipped for large-scale optimization leave substantial room for advancement. To navigate the enormous combinatorial search space, we propose a learning-assisted variable neighborhood search (L-VNS) algorithm integrating four key components. First, an auxiliary task focusing on the upper-level connectivity subproblem generates high-quality initial solutions by employing hybrid genetic search for connection optimization and A...

arXiv Neural/NEabout 2 hours ago
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Learning to Sample in Variable Neighborhood Search Algorithm for Urban Cable Routing Optimization | Steek AI Signal | Steek