Evolutionary Dynamic Multi-objective Optimisation: A Survey

Author:

Jiang Shouyong1ORCID,Zou Juan2ORCID,Yang Shengxiang3ORCID,Yao Xin4ORCID

Affiliation:

1. University of Aberdeen

2. Xiangtan University

3. De Montfort University

4. Southern University of Science and Technology

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.

Funder

National Natural Science Foundation of China

Guangdong Provincial Key Laboratory

Program for Guangdong Introducing Innovative and Enterpreneurial Teams

Shenzhen Science and Technology Program

Research Institute of Trustworthy Autonomous Systems

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

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