Multi-Strategy Coevolving Aging Particle Optimization
- Submitting institution
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De Montfort University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11029
- Type
- D - Journal article
- DOI
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10.1142/S0129065714500087
- Title of journal
- International Journal of Neural Systems
- Article number
- 1450008
- First page
- -
- Volume
- 24
- Issue
- 01
- ISSN
- 0129-0657
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2014
- 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
-
2
- Research group(s)
-
-
- Citation count
- 51
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- When high-performances are needed to optimise engineering systems, off-line solutions come into play to help practitioners achieve their goals. Proudly, our method seems not to be plagued by an increasing number of design variables to be optimised. Indeed, it was shown to be capable of dealing with large-scale problems of different natures, including noisy problems in robotics where the kinematic model of a robotic arm was model via several different artificial neural network of increasing complexity.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -