2021
DOI: 10.1080/09540091.2021.1984396
|View full text |Cite
|
Sign up to set email alerts
|

A hybrid differential evolution for multi-objective optimisation problems

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 9 publications
(1 citation statement)
references
References 37 publications
0
1
0
Order By: Relevance
“…Therefore, there is a rich literature to improve the performance of DE [20]. Likewise, hybridizing DE with other methods is another way to add more power to DE in discovering better PFs [21][22][23]. In our proposed method to optimize the well-known scheduling problem, not only operator improvement but also hybridization are applied to have a robust hybrid system to deal with objective functions (makespan, reliability, and flow time).…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, there is a rich literature to improve the performance of DE [20]. Likewise, hybridizing DE with other methods is another way to add more power to DE in discovering better PFs [21][22][23]. In our proposed method to optimize the well-known scheduling problem, not only operator improvement but also hybridization are applied to have a robust hybrid system to deal with objective functions (makespan, reliability, and flow time).…”
Section: Introductionmentioning
confidence: 99%