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Quick Hypervolume
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OA
AI
DOI：10.1109/TEVC.2013.2281525.png)
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Abstract
En 中文
In this paper, we present a new algorithm for calculating exact hypervolumes. Given a set of d-dimensional points, it computes the hypervolume of the dominated space. Determining this value is an important subroutine of multiobjective evolutionary algorithms. We analyze the quick hypervolume (QHV) algorithm theoretically and experimentally. The theoretical results are a significant contribution to the current state of the art. Moreover, the experimental performance is also very competitive, compared with existing exact hypervolume algorithms.
Keywords:
Diversity methods
hypervolume
multiobjective optimization
performance metrics
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12
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1.9K
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