Multi-Line Distance Minimization: A Visualized Many-Objective Test Problem Suite
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 038-173707-15394
- Type
- D - Journal article
- DOI
-
10.1109/TEVC.2017.2655451
- Title of journal
- Ieee Transactions On Evolutionary Computation
- Article number
- -
- First page
- 61
- Volume
- 22
- Issue
- 1
- ISSN
- 1089-778X
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2017
- URL
-
https://ieeexplore.ieee.org/document/7822978
- Supplementary information
-
https://ieeexplore.ieee.org/ielx7/4235/8272041/7822978/tevc-li-2655451-mm.zip?tp=&arnumber=7822978
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 23
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in the top evolutionary computation journal that was ranked No 1 in Computer Science: Theory and Methods (JCR 2016), the paper substantially extends a paper that won the Best Student Paper award in the 2014 IEEE Congress on Evolutionary Computation, the only award given in this premier conference with a total of 882 submissions. It proposes ML-DMP, a novel class of scalable test problems which have been used as standard benchmark functions by optimisation researchers, and some of the test problems have been used in the Many-objective Optimisation Competition at IEEE CEC2018.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
