Size of this preview: 800 × 529 pixels. Other resolutions: 320 × 212 pixels | 640 × 423 pixels | 1,042 × 689 pixels.
Original file (1,042 × 689 pixels, file size: 148 KB, MIME type: image/jpeg)
 | This is a file from the Wikimedia Commons. Information from its description page there is shown below. Commons is a freely licensed media file repository. You can help. |
 | This image was uploaded in the JPEG format even though it consists of non-photographic data. This information could be stored more efficiently or accurately in the PNG or SVG format. If possible, please upload a PNG or SVG version of this image without compression artifacts, derived from a non-JPEG source (or with existing artifacts removed). After doing so, please tag the JPEG version with {{Superseded|NewImage.ext}} and remove this tag. This tag should not be applied to photographs or scans. If this image is a diagram or other image suitable for vectorisation, please tag this image with {{Convert to SVG}} instead of {{BadJPEG}}. If not suitable for vectorisation, use {{Convert to PNG}}. For more information, see {{BadJPEG}}. |  |
Summary
| Description |
English: Performance landscape showing how a simple Particle Swarm Optimization (PSO) variant performs in aggregate on several benchmark problems when varying two PSO parameters. Lower meta-fitness values means better PSO performance. Such a performance landscape is very time-consuming to compute, especially for optimizers with several behavioural parameters, but it can be searched efficiently using the simple meta-optimization approach by Pedersen implemented in SwarmOps to uncover PSO parameters with good performance. Good choices would here seem to be in the region and , and the region and Deutsch: Gütefunktion, die zeigt wie gut eine einfache Variante der Partikelschwarmoptimierung (PSO) verschiedene Testfunktionen unter Veränderung zweier Parameter insgesamt bearbeitet. Ein kleinerer Meta-Fitness-Wert bedeutet eine bessere Performance der PSO. Eine gute Parameterwahl läge hier in der Region und , und in der Region und |
| Date | |
| Source | Own work |
| Author | Pedersen, M.E.H., Tuning & Simplifying Heuristical Optimization, PhD Thesis, 2010, University of Southampton, School of Engineering Sciences, Computational Engineering and Design Group. |
![{\displaystyle \omega \in [-0.6;0]}](https://wikimedia.org/api/rest_v1/media/math/render/svg/c588933fcee8f837f2e35dc21f66f4e69c22feb9)
![{\displaystyle \phi _{g}\in [2;4]}](https://wikimedia.org/api/rest_v1/media/math/render/svg/c0135cefecb8ab4acae002a011d890cff378554f)
![{\displaystyle \omega \in [-1.25;-0.75]}](https://wikimedia.org/api/rest_v1/media/math/render/svg/caf32e7786f58c641419fc081b5332448b526071)
![{\displaystyle \phi _{g}\in [-3.5;-2]}](https://wikimedia.org/api/rest_v1/media/math/render/svg/d6071d8bc4b72152825ebf4dfae2b56c0f19ef97)
.JPG){kind=link}
.JPG&action=edit&redlink=1){kind=link}
.JPG){kind=link}
Licensing
|
I, the copyright holder of this work, release this work into the public domain. This applies worldwide. In some countries this may not be legally possible; if so: I grant anyone the right to use this work for any purpose, without any conditions, unless such conditions are required by law. |
File history
Click on a date/time to view the file as it appeared at that time.
| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 07:50, 19 March 2010 | | 1,042 × 689 (148 KB) | Optimering | {{Information |Description={{en|1=Performance landscape showing how a simple Particle Swarm Optimization (PSO) variant performs in aggregate on several benchmark problems when varying two PSO parameters. Lower meta-fitness values means better PSO performa |
File usage
The following page uses this file:
Global file usage
The following other wikis use this file:
- Usage on es.wikipedia.org
- Usage on id.wikipedia.org
- Usage on it.wikipedia.org
- Usage on vi.wikipedia.org
.JPG){kind=link}