Evolutionary Dynamic Multi-objective Optimisation: A Survey

Shouyong JIANG, Juan ZOU, Shengxiang YANG, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

43 Citations (Scopus)


Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly growing area of investigation. EDMO employs evolutionary approaches to handle multi-objective optimisation problems that have time-varying changes in objective functions, constraints, and/or environmental parameters. Due to the simultaneous presence of dynamics and multi-objectivity in problems, the optimisation difficulty for EDMO has a marked increase compared to that for single-objective or stationary optimisation. After nearly two decades of community effort, EDMO has achieved significant advancements on various topics, including theoretic research and applications. This article presents a broad survey and taxonomy of existing research on EDMO. Multiple research opportunities are highlighted to further promote the development of the EDMO research field.

Original languageEnglish
Article number76
Pages (from-to)1-47
Number of pages47
JournalACM Computing Surveys
Issue number4
Publication statusPublished - Nov 2022
Externally publishedYes

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  • dynamic environment
  • evolutionary algorithm
  • evolutionary dynamic multi-objective optimisation
  • Multi-objective optimisation


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