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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Particle Swarm Optimizer with C-Pg Mutation
verfasst von : Guojiang Fu, Shaomei Wang, Mingjun Chen, Ning Li
Erschienen in: Computational Intelligence and Security
Verlag: Springer Berlin Heidelberg
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This paper presents a modified PSO algorithm, called the PSO with
C-Pg
mutation, or PSOWC-Pg, the algorithm adopts
C-Pg
mutation, the idea is to replace global optimal point
gBest
with disturbing point
C
and
gBest
alternately in the original formulae, the probability of using
C
is
R
. There are two methods for selecting
C
: stochastic method and the worst fitness method. The stochastic method selects some particle’s current position
x
or
pBest
as
C
stochastically in each iteration loop, the worst fitness method selects the worst particle’s
x
or the
pBest
of some particle with the worst fitness value as
C
. So, when
R
is small enough, the distance between
C
and
gBest
will tend towards 0, particle swarm will converge slowly and irregularly. The results of experiments show that PSOWC-Pg exhibit excellent performance for test functions.