Abstract
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 language | English |
|---|---|
| Article number | 76 |
| Pages (from-to) | 1-47 |
| Number of pages | 47 |
| Journal | ACM Computing Surveys |
| Volume | 55 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Nov 2022 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2022 Copyright held by the owner/author(s).
Funding
This work was supported by National Natural Science Foundation of China (Grant No. 61876164), Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531), and the Research Institute of Trustworthy Autonomous Systems.
Keywords
- dynamic environment
- evolutionary algorithm
- evolutionary dynamic multi-objective optimisation
- Multi-objective optimisation