On the use of hypervolume for diversity measurement of Pareto front approximations

Shouyong Jiang, Shengxiang Yang, Miqing Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

In multiobjective optimization, a good quality indicator is of great importance to the performance assessment of algorithms. This paper investigates the effectiveness of the widely-used hypervolume indicator, which is the only one found so far to strictly comply with the Pareto dominance. While hypervolume is of undisputed success to assess the quality of an approximation, it is sensitive to misleading cases, particularly for diversity assessment. To address this issue, this paper presents a modified hypervolume indicator based on linear projection for diversity evaluation. In addition to experimental studies to demonstrate the effectiveness of the proposed indicator, the indicator is introduced into the environmental selecction of an indicator-based multiobjective optimization evolutionary algorithm. Experiments show that the proposed indicator yields more evenly-distributed approximations than the original hypervolume indicator.

Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509042401
DOIs
Publication statusPublished - 9 Feb 2017
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece
Duration: 6 Dec 20169 Dec 2016

Publication series

Name2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Country/TerritoryGreece
CityAthens
Period6/12/169/12/16

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