A hierarchical particle swarm optimizer for noisy and
dynamic environments
Genetic Programming and Evolvable Machines, 7 (4):
329--354 (December 2006)DOI: doi:10.1007/s10710-006-9014-6
Abstract
New Particle Swarm Optimisation (PSO) methods for
dynamic and noisy function optimisation are studied in
this paper. The new methods are based on the
hierarchical PSO (H-PSO) and a new type of H-PSO
algorithm, called Partitioned Hierarchical PSO
(PH-PSO). PH-PSO maintains a hierarchy of particles
that is partitioned into several sub-swarms for a
limited number of generations after a change of the
environment occurred. Different methods for determining
the best time when to rejoin the sub-swarms and how to
handle the topmost sub-swarm are discussed. A standard
method for metaheuristics to cope with noise is to use
function re-evaluations. To reduce the number of
necessary re-evaluations a new method is proposed here
which uses the hierarchy to find a subset of particles
for which re-evaluations are particularly important. In
addition, a new method to detect changes of the
optimization function in the presence of noise is
presented. It differs from conventional detection
methods because it does not require additional function
evaluations. Instead it relies on observations of
changes that occur within the swarm hierarchy. The new
algorithms are compared experimentally on different
dynamic and noisy benchmark functions with a variant of
standard PSO and H-PSO that are both provided with a
change detection and response method.