Particle swarm optimization (PSO) is a nature-inspired technique for solving
continuous optimization problems.
For a fixed optimization problem, the quality of the found solution depends
significantly on the choice of the algorithmic PSO parameters such
as the inertia weight and the acceleration coefficients.
It is a challenging task to choose appropriate values for these
parameters by hand or mathematically.
In this paper, a novel self-optimizing particle swarm optimizer with
multiple adaptation layers is introduced.
In the new algorithm, adaptation takes place on both particle and subswarm level
The new idea of using virtual parameter swarms
modifiable parameter configurations each is introduced.
The algorithmic PSO parameters can be mutated by
using, for instance, well-known techniques from the field of evolutionary algori
thms, in order to allow
fine-granular parameter adaptation to the problem at hand.
The new algorithm is experimentally evaluated, and compared to
a standard PSO and the TRIBES algorithm.
The experimental study shows that our new algorithm is highly competitive to
previously suggested approaches.