A new dominance relation-based evolutionary algorithm for many-objective optimization
- Submitting institution
-
The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 91625317
- Type
- D - Journal article
- DOI
-
10.1109/TEVC.2015.2420112
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 16
- Volume
- 20
- Issue
- 1
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- 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
-
3
- Research group(s)
-
-
- Citation count
- 290
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In multi-objective optimisation, the Pareto dominance relation is fundamental for comparing solutions. However, it is too restrictive when the number of objectives increases since it requires "improvement" on all objectives. This work introduces a novel dominance relation to tackle this issue. Using it, the also presented algorithm provides a good balance between convergence and diversity, the two basic goals of multi-objective optimisation. The results were successfully validated on a range of problems with up to 15 objectives.
The paper was published in the top evolutionary computation journal and is a "Highly Cited Paper" (Web of Science).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
