An empirical study of dynamic triobjective optimisation problems

Date

2018-07

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE Press

Type

Conference

Peer reviewed

Yes

Abstract

Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic multiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics.

Description

Keywords

Citation

Jiang, S. et al. (2018) An empirical study of dynamic triobjective optimisation problems. Proceedings of the 2018 IEEE Congress on Evolutionary Computation, 2018

Rights

Research Institute

Institute of Artificial Intelligence (IAI)