Quflow has experience from training and optimization of neural networks for human factor modeling, with surprisingly good results (http://quflow.com/publications). Recently, Fang et al. (http://tr.ietejournals.org/text.asp?2010/27/4/336/64601) listed how similar Quantum Particle Swarm Optimization (QPSO) methods has been used to successfully optimize global system performance in many engineering disciplines.
Nowadays, optimization is often integrated and relatively easy to use in most design tools. Designers can therefore focus more on analyzing which parameters matter most and let the tool do its job finding a global optimum configuration. This works well within each specific design discipline, however a complete product requires optimization across multiple disciplines simultaneously, and few tools, engineers and application examples exist for this. It would be interesting to see how such products would look like – probably much more organic and chaotic than we are used to, as in the 100% computer-generated WiFi antenna pattern below (from the last reference below) which works very well but raises questions on how it actually works. Optimization is fun, creative and easily leads to completely new ideas and understandings – very much Quflow.
- PSO for Python: http://playdoh.googlecode.com.
- Using PSO http://www.radioeng.cz/fulltexts/2005/05_04_091_097.pdf.
- Using QPSO http://arxiv.org/ftp/physics/papers/0702/0702214.pdf,
- Other methods http://www.nabfastroad.org/NABHighperformanceIndoorTVantennaRpt.pdf.