File:PSO Meta-Fitness Landscape (12 benchmark problems).JPG
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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)
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| current | 07:50, 19 March 2010 | | 1,042 × 689 (148 KB) | Optimering (talk | contribs) | {{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 |
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