A Box Decomposition Algorithm to Compute the Hypervolume Indicator
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Abstract (arXiv)
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 fracp-12 rfloor+1) and O(n^lfloor fracp2 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(np-1) and a lower bound of Omega (n^lfloor fracp2rfloor log n ) for p ≥ 4 on the worst-case complexity of the WFG algorithm.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Oct 2015.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 2; 2024: 0; 2025: 1.
9 papers.
- 2021Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvementcited by 118
- 2022Joint Entropy Search for Multi-objective Bayesian Optimizationcited by 45
- 2022Robust Multi-Objective Bayesian Optimization Under Input Noisecited by 37
- 2023qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson samplingcited by 11
- 2022PF2ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimizationcited by 5
- 2023Knowledge Gradient for Multi-Objective Bayesian Optimization with Decoupled Evaluationscited by 5
- 2021What if we Increase the Number of Objectives? Theoretical and Empirical Implications for Many-objective Optimizationcited by 0
- 2022Multi-objective optimization via equivariant deep hypervolume approximationcited by 0
- 2025Approximation of Box Decomposition Algorithm for Fast Hypervolume-Based Multi-Objective Optimizationcited by 0
- 2025Approximation of Box Decomposition Algorithm for Fast Hypervolume-Based Multi-Objective Optimization5 Dec 2025
- 2023qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling24 Oct 2023
- 2023Knowledge Gradient for Multi-Objective Bayesian Optimization with Decoupled Evaluations2 Feb 2023
- 2022Joint Entropy Search for Multi-objective Bayesian Optimization6 Oct 2022
- 2022Multi-objective optimization via equivariant deep hypervolume approximation5 Oct 2022
- 2022PF2ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization11 Apr 2022
- 2022Robust Multi-Objective Bayesian Optimization Under Input Noise15 Feb 2022
- 2021What if we Increase the Number of Objectives? Theoretical and Empirical Implications for Many-objective Optimization6 Jun 2021
- 2021Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement17 May 2021