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Computers & Operations Research
Article . 2017 . Peer-reviewed
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A box decomposition algorithm to compute the hypervolume indicator

Authors: Renaud Lacour; Kathrin Klamroth; Carlos M. Fonseca;

A box decomposition algorithm to compute the hypervolume indicator

Abstract

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 $O(n^{\lfloor \frac{p-1}{2} \rfloor+1})$ and $O(n^{\lfloor \frac{p}{2} \rfloor+1})$, respectively. 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 $��(n^{\lfloor \frac{p}{2}\rfloor} \log n )$ for $p \geq 4$ on the worst-case complexity of the WFG algorithm.

21 pages

Keywords

FOS: Computer and information sciences, Discrete Mathematics (cs.DM), Integer programming, multi-objective optimization, Numerical aspects of computer graphics, image analysis, and computational geometry, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS), Multi-objective and goal programming, hypervolume indicator, Klee's measure problem, Computer Science - Discrete Mathematics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
56
Top 10%
Top 10%
Top 10%
Green
bronze