Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons

Shouyong Jiang, Shengxiang Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

84 Citations (Scopus)

Abstract

Dynamic multiobjective optimization (DMO) has received growing research interest in recent years since many real-world optimization problems appear to not only have multiple objectives that conflict with each other but also change over time. The time-varying characteristics of these DMO problems (DMOPs) pose new challenges to evolutionary algorithms. Considering the importance of a representative and diverse set of benchmark functions for DMO, in this paper, we propose a new benchmark generator that is able to tune a number of challenging characteristics, including mixed Pareto-optimal front (convexity-concavity), nonmonotonic and time-varying variable-linkages, mixed types of changes, and randomness in type change, which have rarely or not been considered or tested in the literature. A test suite of ten instances with different dynamic features is produced from the generator in this paper. Additionally, a few new performance measures are proposed to evaluate algorithms for DMOPs with different characteristics. Six representative multiobjective evolutionary algorithms from the literature are investigated based on the proposed DMO test suite and performance measures. The experimental results facilitate a better understanding of strengths and weaknesses of these compared algorithms for DMOPs.

Original languageEnglish
Pages (from-to)198-211
Number of pages14
JournalIEEE Transactions on Cybernetics
Volume47
Issue number1
Early online date13 Jan 2016
DOIs
Publication statusPublished - Jan 2017

Keywords

  • Benchmark
  • dynamic multiobjective optimization (DMO)
  • evolutionary algorithm
  • performance metric

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