A benchmark generator for dynamic multi-objective optimization problems

Shouyong Jiang, Shengxiang Yang

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

8 Citations (Scopus)

Abstract

Many real-world optimization problems appear to not only have multiple objectives that conflict each other but also change over time. They are dynamic multi-objective optimization problems (DMOPs) and the corresponding field is called dynamic multi-objective optimization (DMO), which has gained growing attention in recent years. However, one main issue in the field of DMO is that there is no standard test suite to determine whether an algorithm is capable of solving them. This paper presents a new benchmark generator for DMOPs that can generate several complicated characteristics, including mixed Pareto-optimal front (convexity-concavity), strong dependencies between variables, and a mixed type of change, which are rarely tested in the literature. Experiments are conducted to compare the performance of five state-of-the-art DMO algorithms on several typical test functions derived from the proposed generator, which gives a better understanding of the strengths and weaknesses of these tested algorithms for DMOPs.

Original languageEnglish
Title of host publication2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings
EditorsDaniel Neagu, Mariam Kiran, Paul Trundle
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479955381
DOIs
Publication statusPublished - 17 Oct 2014
Event2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Bradford, West Yorkshire, United Kingdom
Duration: 8 Sep 201410 Sep 2014

Publication series

Name2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings

Conference

Conference2014 14th UK Workshop on Computational Intelligence, UKCI 2014
Country/TerritoryUnited Kingdom
CityBradford, West Yorkshire
Period8/09/1410/09/14

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