Evolutionary dynamic multi-objective optimization: A survey

Date

2022-03

Advisors

Journal Title

Journal ISSN

ISSN

0360-0300

Volume Title

Publisher

ACM Press

Type

Article

Peer reviewed

Yes

Abstract

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 existing research on EDMO. As a result, multiple future research directions are highlighted to further promote the development of the EDMO research field.

Description

The file attached to this record is the author's final peer reviewed version.

Keywords

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

Citation

Jiang, S., Zou, J., Yang, S. and Yao, X. (2022) Evolutionary dynamic multi-objective optimization: A survey. ACM Computing Survey.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)