Evolutionary Dynamic Multi-Objective Optimisation: A survey

Shouyong Jiang* (Corresponding Author), Juan Zou, Shengxiang Yang, Xin Yao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Downloads (Pure)


Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young area of investigation that is rapidly growing. 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 effect, EDMO has achieved significant advancements on various topics, including dynamics characterisation, change detection, change response, performance assessment. In addition, there have been a number of studies on application of EDMO to real-world problems. This paper presents a broad survey and taxonomy of exist- ing research on EDMO. As a result, multiple future research directions are highlighted to further promote the development of the EDMO research field.
Original languageEnglish
JournalACM Computing Surveys
Early online date28 Mar 2022
Publication statusE-pub ahead of print - 28 Mar 2022


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


Dive into the research topics of 'Evolutionary Dynamic Multi-Objective Optimisation: A survey'. Together they form a unique fingerprint.

Cite this