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.
Bibliographical noteThis 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.
- Multi-objective optimisation
- evolutionary algorithm
- dynamic environment
- evolutionary dynamic multi-objective optimisation