Multiobject spectroscopy is applied in numerous modern astronomical facilities conducting observations of a large number of targets per pointing. Assigning the maximum number of targets to these instruments requires efficient algorithms. We present a simple and effective algorithm, the averaging (Aver) algorithm, to maximize the number of assigned targets for the first few visits of a given field. In comparison to the draining (Dra) algorithm, our algorithm increases the target completeness by 1% to 2% by employing Poisson distributed and real catalogs from the Large Sky Area Multiobject Fiber Spectroscopic Telescope survey. Moreover, our algorithm performs ∼375 times faster than the conventionally applied simulated annealing algorithm and yields a slightly higher completeness. We further optimize the Aver and Dra algorithms by combining the genetic algorithm (GA) and the differential evolution method. The Aver is slightly optimized by this method, whereas the Dra algorithm is improved by 0.9% to 1.6%, suggesting that our proposed Aver algorithm approaches maximum completeness. Furthermore, we find that the GA can optimize the rotation angle with a specially designed fitness function in the case of focal-plane rotation that is expected to be realized in the future, achieving a 1.8% increase in the number of the targets observed. In particular, our Aver algorithm assigns the maximum number of targets within the first few visits.

中文翻译：

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宽视野多目标光纤馈电光谱仪中的光纤分配

多目标光谱技术已应用于许多现代天文设施中，可对每个指向进行大量目标观测。为这些仪器分配最大目标数量需要高效的算法。我们提出一种简单有效的算法，即平均（Aver）算法，以在给定字段的前几次访问中最大化分配的目标数量。与排放（Dra）算法相比，我们的算法通过使用大天空区域多目标光纤光谱望远镜勘测的泊松分布和真实目录将目标完整性提高了1％至2％。此外，我们的算法的执行速度比传统应用的模拟退火算法快375倍，并且完整性更高。通过结合遗传算法（GA）和差分进化方法，我们进一步优化了Aver和Dra算法。该方法对Aver进行了稍微优化，而Dra算法则提高了0.9％至1.6％，这表明我们提出的Aver算法达到了最大完整性。此外，我们发现，在焦平面旋转的情况下，GA可以通过特殊设计的适应度函数优化旋转角度，预计将来会实现这一目标，从而使观察到的目标数量增加了1.8％。特别是，我们的Aver算法会在前几次访问中分配最大目标数。这表明我们提出的Aver算法接近最大完整性。此外，我们发现，在焦平面旋转的情况下，GA可以通过特殊设计的适应度函数优化旋转角度，这有望在将来实现，从而使观察到的目标数量增加1.8％。特别是，我们的Aver算法会在前几次访问中分配最大数量的目标。这表明我们提出的Aver算法接近最大完整性。此外，我们发现，在焦平面旋转的情况下，GA可以通过特殊设计的适应度函数优化旋转角度，预计将来会实现这一目标，从而使观察到的目标数量增加了1.8％。特别是，我们的Aver算法会在前几次访问中分配最大数量的目标。