Simulation-guided Beam Search for Neural Combinatorial Optimization

• URL: http://arxiv.org/abs/2207.06190v1
• Date: Wed, 13 Jul 2022 13:34:35 GMT
• Title: Simulation-guided Beam Search for Neural Combinatorial Optimization
• Authors: Jinho Choo, Yeong-Dae Kwon, Jihoon Kim, Jeongwoo Jae, Andr\'e Hottung, Kevin Tierney, Youngjune Gwon
• Abstract summary: We propose simulation-guided beam search (SGBS) for neural optimization problems. We hybridize SGBS with efficient active search (EAS), where SGBS enhances the quality of solutions backpropagated in EAS. We evaluate our methods on well-known CO benchmarks and show that SGBS significantly improves the quality of the solutions found under reasonable assumptions.
• Score: 13.072343634530883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems. While neural approaches capable of high-quality solutions in a single shot are emerging, state-of-the-art approaches are often unable to take full advantage of the solving time available to them. In contrast, hand-crafted heuristics perform highly effective search well and exploit the computation time given to them, but contain heuristics that are difficult to adapt to a dataset being solved. With the goal of providing a powerful search procedure to neural CO approaches, we propose simulation-guided beam search (SGBS), which examines candidate solutions within a fixed-width tree search that both a neural net-learned policy and a simulation (rollout) identify as promising. We further hybridize SGBS with efficient active search (EAS), where SGBS enhances the quality of solutions backpropagated in EAS, and EAS improves the quality of the policy used in SGBS. We evaluate our methods on well-known CO benchmarks and show that SGBS significantly improves the quality of the solutions found under reasonable runtime assumptions.

Related papers

• Neural Solver Selection for Combinatorial Optimization [23.449310200885893]
  We propose the first general framework to coordinate neural solvers, which involves feature extraction, selection model, and selection strategy. We show that the proposed framework can effectively distribute instances and the resulting composite solver can achieve significantly better performance.
  arXiv  Detail & Related papers  (2024-10-13T02:05:41Z)
• LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning [56.273799410256075]
  The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to optimize the reasoning path. The framework has been tested on general and advanced benchmarks, showing superior performance in terms of search efficiency and problem-solving capability.
  arXiv  Detail & Related papers  (2024-10-03T18:12:29Z)
• Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization [15.476478159958416]
  We employ a large language model (LLM) to enhance evolutionary search for solving constrained multi-objective optimization problems. Our aim is to speed up the convergence of the evolutionary population.
  arXiv  Detail & Related papers  (2024-05-09T13:44:04Z)
• BalMCTS: Balancing Objective Function and Search Nodes in MCTS for Constraint Optimization Problems [7.196057722218442]
  Constraint Problems Optimization (COP) pose intricate challenges in problems usually addressed through Branch and Bound (B&B) methods. We propose a novel neural network algorithm based on a depth-first search algorithm for solving COP. Our method identifies feasible solutions with a gap of less than 17.63% within the initial 5 feasible solutions.
  arXiv  Detail & Related papers  (2023-12-26T03:09:08Z)
• MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks [63.24965775030673]
  We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
  arXiv  Detail & Related papers  (2023-02-02T18:27:20Z)
• High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine for NOMA Systems [3.6406488220483326]
  A key challenge to fully utilizing the effectiveness of the NOMA technique is the optimization of the resource allocation. We propose the coherent Ising machine (CIM) based optimization method for channel allocation in NOMA systems. We show that our proposed method is superior in terms of speed and the attained optimal solutions.
  arXiv  Detail & Related papers  (2022-12-03T09:22:54Z)
• High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series [1.9371782627708491]
  We propose a new high dimensional Bayesian Optimization algorithm combining Recurrent neural networks. The proposed RNN-BO algorithm can solve the optimal control problems in the lower dimension space. We also discuss the impacts of different numbers of the RNN layers and training epochs on the trade-off between solution quality and related computational efforts.
  arXiv  Detail & Related papers  (2022-01-01T08:40:17Z)
• Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
  We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC. We provide a practical parametrization of the core optimization problem.
  arXiv  Detail & Related papers  (2021-07-08T18:01:02Z)
• Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications [119.81868223344173]
  The paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications. Under this framework, we propose two algorithms that solve FSO resource allocation problems.
  arXiv  Detail & Related papers  (2020-07-27T17:38:51Z)
• Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems [91.43582419264763]
  We study model-based reinforcement learning (RL) in unknown stabilizable linear dynamical systems. We propose an algorithm that certifies fast stabilization of the underlying system by effectively exploring the environment. We show that the proposed algorithm attains $tildemathcalO(sqrtT)$ regret after $T$ time steps of agent-environment interaction.
  arXiv  Detail & Related papers  (2020-07-23T23:06:40Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.