A box decomposition algorithm to compute the hypervolume indicator. (March 2017)

• Record Type: Journal Article
• Authors: Lacour, Renaud; Klamroth, Kathrin; Fonseca, Carlos M.
• Is Part Of: Computers & operations research. Volume 79(2017)
• Page Start: 347
• Page End: 360
• Publication Date: 2017-03
• Subjects: Multi-objective optimization -- Hypervolume indicator -- Klee's measure problem; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2016.06.021
• Languages: English
• ISSNs: 0305-0548

Abstract

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 ⌊ p − 1 2 ⌋ + 1 ) and O ( n ⌊ p 2 ⌋ + 1 ), respectively, where n is the number of points and p is the dimension of the objective space. 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 Ω ( n ⌊ p 2 ⌋ log n ) for p ≥ 4 on the worst-case complexity of the WFG algorithm. Abstract : Highlights: We propose a theoretically and practically efficient method to compute hypervolumes. We partition the dominated region into axis-parallel hyperrectangles or boxes. We provide a refined analysis of the complexity of the WFG algorithm.