Title: Parameter Adaptation and Criticality in Particle Swarm Optimization,
Abstract: Generality is one of the main advantages of heuristic algorithms, as such,
multiple parameters are exposed to the user with the objective of allowing them
to shape the algorithms to their specific needs. Parameter selection,
therefore, becomes an intrinsic problem of every heuristic algorithm. Selecting
good parameter values relies not only on knowledge related to the problem at
hand, but to the algorithms themselves. This research explores the usage of
self-organized criticality to reduce user interaction in the process of
selecting suitable parameters for particle swarm optimization (PSO) heuristics.
A particle swarm variant (named Adaptive PSO) with self-organized criticality
is developed and benchmarked against the standard PSO. Criticality is observed
in the dynamic behaviour of this swarm and excellent results are observed in
the long run. In contrast with the standard PSO, the Adaptive PSO does not
stagnate at any point in time, balancing the concepts of exploration and
exploitation better. A software platform for experimenting with particle
swarms, called PSO Laboratory, is also developed. This software is used to test
the standard PSO as well as all other PSO variants developed in the process of
creating the Adaptive PSO. As the software is intended to be of aid to future
and related research, special attention has been put in the development of a
friendly graphical user interface. Particle swarms are executed in real time,
allowing users to experiment by changing parameters on-the-fly.