
swarm population can be conceptualized as cells in a CA, whose states
change in many dimensions simultaneously.
Particle swarm optimization is powerful, easy to understand, easy to
implement, and computationally efficient. The central algorithm com-
prises just two lines of computer code and is often at least an order of
magnitude faster than other evolutionary algorithms on benchmark
functions. It is extremely resistant to being trapped in local optima.
As an engineering methodology, particle swarm optimization has
been applied to fields as diverse as electric/hybrid vehicle battery pack
state of charge, human performance assessment, and human tremor ...