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.
|Title of host publication||2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 28 Sept 2018|
|Event||2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil|
Duration: 8 Jul 2018 → 13 Jul 2018
|Name||2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings|
|Conference||2018 IEEE Congress on Evolutionary Computation, CEC 2018|
|City||Rio de Janeiro|
|Period||8/07/18 → 13/07/18|
Bibliographical noteFunding Information:
This work was supported in part by the Engineering and Physical Sciences Research Council of U.K. under the project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)” , and in part by the National Natural Science Foundation of China under Grant 61673331.
© 2018 IEEE.