𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Dynamic analysis for the selection of parameters and initial population, in particle swarm optimization

✍ Scribed by Emilio F. Campana; Giovanni Fasano; Antonio Pinto

Publisher
Springer US
Year
2009
Tongue
English
Weight
649 KB
Volume
48
Category
Article
ISSN
0925-5001

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

The particle swarm optimization algorith
The particle swarm optimization algorithm: convergence analysis and parameter selection
✍ Ioan Cristian Trelea πŸ“‚ Article πŸ“… 2003 πŸ› Elsevier Science 🌐 English βš– 162 KB

The particle swarm optimization algorithm is analyzed using standard results from the dynamic system theory. Graphical parameter selection guidelines are derived. The exploration–exploitation tradeoff is discussed and illustrated. Examples of performance on benchmark functions superior to previously

Particle swarm optimization in the deter
Particle swarm optimization in the determination of the optimal bias current for noise performance of gallium nitride HEMTs
✍ Tyler Ross; Gabriel Cormier; Khelifa Hettak; Rony E. Amaya πŸ“‚ Article πŸ“… 2011 πŸ› John Wiley and Sons 🌐 English βš– 341 KB

## Abstract Gallium nitride has attracted a great deal of interest in recent years due to its power handling ability. In addition, its noise performance is known to be good. In this letter, we present a method for determining the bias current density needed to obtain optimal noise figure for galliu

The Design of Adaptive Systems: Optimal
The Design of Adaptive Systems: Optimal Parameters for Variation and Selection in Learning and Development
✍ Steven A. Frank πŸ“‚ Article πŸ“… 1997 πŸ› Elsevier Science 🌐 English βš– 478 KB

Some aspects of learning and development are based on evolutionary change within the organism. In trial and error learning, variant ideas or behaviors are generated and selective filters (learning rules) choose among the population of variants. Development may, in some cases, proceed by selection wi