Bandit-based Variable Fixing for Binary Optimization on GPU Parallel Computing

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This paper explores whether reinforcement learning is capable of enhancing metaheuristics for the quadratic unconstrained binary optimization (QUBO), which have recently attracted attention as a solver for a wide range of combinatorial optimization problems. In particular, we introduce a novel approach called the bandit-based variable fixing (BVF). The key idea behind BVF is to regard an execution of an arbitrary metaheuristic with a variable fixed as a play of a slot machine. Thus, BVF explores variables to fix with the maximum expected reward, and executes a metaheuristic at the same time. The bandit-based approach is then extended to fix multiple variables. To accelerate solving multi-armed bandit problem, we implement a parallel algorithm for BVF on a GPU. Our results suggest that our proposed BVF enhances original metaheuristics.

31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 01-03 March 2023, Naples, Italy.

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  • CRID
    1050015333085580416
  • HANDLE
    2433/284024
  • 本文言語コード
    en
  • 資料種別
    conference paper
  • データソース種別
    • IRDB

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