Quadratic Unconstrained Binary Optimization (QUBO) is a combinatorial optimization to find an optimal binary solution vector that minimizes the energy value defined by a quadratic formula of binary variables in the vector. As many NP-hard problems can be reduced to QUBO problems, considerable research has gone into developing QUBO solvers running on various computing platforms such as quantum devices, ASICs, FPGAs, GPUs, and optical fibers. This paper presents a framework called Diverse Adaptive Bulk Search (DABS), which has the potential to find optimal solutions of many types of QUBO problems. Our DABS solver employs a genetic algorithm-based search algorithm featuring three diverse strategies: multiple search algorithms, multiple genetic operations, and multiple solution pools. During the execution of the solver, search algorithms and genetic operations that succeeded in finding good solutions are automatically selected to obtain better solutions. Moreover, search algorithms traverse between different solution pools to find good solutions. We have implemented our DABS solver to run on multiple GPUs. Experimental evaluations using eight NVIDIA A100 GPUs confirm that our DABS solver succeeds in finding optimal or potentially optimal solutions for three types of QUBO problems.

翻译：二次无约束二进制优化（QUBO）是一种组合优化问题，旨在寻找最优二进制解向量，以最小化由该向量中二进制变量二次公式定义的能量值。由于许多NP难问题可简化为QUBO问题，大量研究致力于开发运行在量子设备、ASIC、FPGA、GPU和光纤等多种计算平台上的QUBO求解器。本文提出了一种名为多样化自适应批量搜索（DABS）的框架，该框架具有为多种QUBO问题找到最优解的潜力。我们的DABS求解器采用基于遗传算法的搜索算法，包含三种多样化策略：多种搜索算法、多种遗传操作和多个解池。在求解器执行过程中，能够成功找到优质解的搜索算法和遗传操作会被自动选择，以获得更优解。此外，搜索算法会在不同解池之间遍历以寻找优质解。我们在多GPU上实现了DABS求解器。使用八块NVIDIA A100 GPU进行的实验评估证实，我们的DABS求解器成功为三类QUBO问题找到了最优解或潜在最优解。