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Heuristic algorithms based on deep reinforcement learning for quadratic unconstrained binary optimization
الگوریتم های ابتکاری مبتنی بر یادگیری تقویتی عمیق برای بهینه سازی باینری بدون محدودیت درجه دوم-2020 The unconstrained binary quadratic programming (UBQP) problem is a difficult combinatorial optimization
problem that has been intensively studied in the past decades. Due to its NP-hardness, many
heuristic algorithms have been developed for the solution of the UBQP. These algorithms are usually
problem-tailored, which lack generality and scalability. To address these issues, a heuristic algorithm
based on deep reinforcement learning (DRLH) is proposed in this paper. It features in inputting
specific features and using a neural network model called NN to guild the selection of variable at
each solution construction step. Also, to improve the algorithm speed and efficiency, two algorithm
variants named simplified DRLH (DRLS) and DRLS with hill climbing (DRLS-HC) are developed as
well. These three algorithms are examined through extensive experiments in comparison with famous
heuristic algorithms from the literature. Experimental results show that the DRLH, DRLS, and DRLS-HC
outperform their competitors in terms of both solution quality and computational efficiency. Precisely,
the DRLH achieves the best-quality results, while DRLS offers a high-quality solution in a very short
time. By adding a hill-climbing procedure to DRLS, the resulting DRLS-HC algorithm is able to obtain
almost the same quality result as DRLH with however 5 times less computing time on average. We
conducted additional experiments on large-scale instances and various data distributions to verify the
generality and scalability of the proposed algorithms, and the results on benchmark instances indicate
the ability of the algorithms to be applied to practical problems. Keywords: Unconstrained binary quadratic | programming | Heuristic algorithm | Deep reinforcement learning | Neural network |
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