A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization
- Submitting institution
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The University of Surrey
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9014494_3
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2016.2519378
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 773
- Volume
- 20
- Issue
- 5
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2016
- URL
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-
- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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-
- Research group(s)
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- Citation count
- 379
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Although evolutionary algorithms have been shown to be powerful for solving two or three objective optimization problems, they become much less efficient when the number of the objectives is higher. This work suggests an evolutionary algorithm using reference vectors, which can be conveniently specified by users in the objective space, to solve optimization problems with two to over twenty objectives. This algorithm has not only been closely followed by many researchers, but also successfully applied by engineers at Honda to optimize a 7-objective controller of hybrid vehicles and a 20-objecitve vehicle dynamics optimization problem.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -