Quick HyperVolume
This addresses a key computational bottleneck in MOEAs, but appears incremental as it builds on prior exact hypervolume algorithms.
The paper tackles the problem of computing exact hypervolumes for sets of d-dimensional points, which is crucial for Multiobjective Evolutionary Algorithms (MOEAs), and presents the Quick Hypervolume (QHV) algorithm that shows competitive experimental performance compared to existing methods.
We present a new algorithm to calculate 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 (MOEAs). 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. A full description of the algorithm is currently submitted to IEEE Transactions on Evolutionary Computation.