Efficient computation of expected hypervolume improvement using box decomposition algorithms

Kaifeng Yang, Michael Emmerich, André Deutz, Thomas Bäck

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


In the field of multi-objective optimization algorithms, multi-objective Bayesian Global Optimization (MOBGO) is an important branch, in addition to evolutionary multi-objective optimization algorithms. MOBGO utilizes Gaussian Process models learned from previous objective function evaluations to decide the next evaluation site by maximizing or minimizing an infill criterion. A commonly used criterion in MOBGO is the Expected Hypervolume Improvement (EHVI), which shows a good performance on a wide range of problems, with respect to exploration and exploitation. However, so far, it has been a challenge to calculate exact EHVI values efficiently. This paper proposes an efficient algorithm for the exact calculation of the EHVI for in a generic case. This efficient algorithm is based on partitioning the integration volume into a set of axis-parallel slices. Theoretically, the upper bound time complexities can be improved from previously O(n2) and O(n3) , for two- and three-objective problems respectively, to Θ(nlog n) , which is asymptotically optimal. This article generalizes the scheme in higher dimensional cases by utilizing a new hyperbox decomposition technique, which is proposed by Dächert et al. (Eur J Oper Res 260(3):841–855, 2017). It also utilizes a generalization of the multilayered integration scheme that scales linearly in the number of hyperboxes of the decomposition. The speed comparison shows that the proposed algorithm in this paper significantly reduces computation time. Finally, this decomposition technique is applied in the calculation of the Probability of Improvement (PoI).

Original languageEnglish
Pages (from-to)3-34
Number of pages32
JournalJournal of Global Optimization
Issue number1
Publication statusPublished - 1 Sept 2019


  • Expected hypervolume improvement
  • Hypervolume indicator
  • Kriging
  • Multi-objective Bayesian global optimization
  • Probability of improvement
  • Time complexity


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