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A box decomposition algorithm to compute the hypervolume indicator

机译:一种用于计算超量指标的盒分解算法

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We propose a new approach to the computation of the hypervolume indicator, based on partitioning the dominated region into a set of axis-parallel hyperrectangles or boxes. We present a nonincremental algorithm and an incremental algorithm, which allows insertions of points, whose time complexities are O(n[p-1/2]+1) and O(n[p/2]+1), respectively, where n is the number of points and p is the dimension of the objective space. While the theoretical complexity of such a method is lower bounded by the complexity of the partition, which is, in the worst-case, larger than the best upper bound on the complexity of the hypervolume computation, we show that it is practically efficient. In particular, the nonincremental algorithm competes with the currently most practically efficient algorithms. Finally, we prove an enhanced upper bound of O(n(P-1)) and a lower bound of Omega(n[p/2]logn) for p >= 4 on the worst-case complexity of the WFG algorithm. (C) 2016 Elsevier Ltd. All rights reserved.
机译:我们基于将主导区域划分为一组轴平行的超矩形或矩形框,提出了一种计算超量指标的新方法。我们提出了一种非增量算法和增量算法,该算法允许插入点,其时间复杂度分别为O(n [p-1 / 2] +1)和O(n [p / 2] +1),其中n是点数,p是目标空间的维数。尽管这种方法的理论复杂度受分区复杂度的下限限制,在最坏的情况下,该上限比超容量计算的复杂度的最佳上限大,但我们证明了该方法实际上是有效的。特别地,非增量算法与当前最实用的算法竞争。最后,我们证明了在WFG算法的最坏情况下,对于p> = 4,O(n(P-1))的上限提高了,而Omega(n [p / 2] logn)的下限得到了提高。 (C)2016 Elsevier Ltd.保留所有权利。

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