IET Renewable Power Generation,
Год журнала:
2022,
Номер
16(8), С. 1535 - 1561
Опубликована: Март 25, 2022
Abstract
In
this
paper,
an
efficient
sine
cosine
differential
gradient‐based
optimization
method
is
proposed
for
identifying
unknown
parameters
of
photovoltaic
models.
the
simulation,
parameter
identification
formulated
as
objective
function
to
be
minimized
based
on
error
between
estimated
and
experimental
data.
Based
original
method,
combines
mutation
crossover
evolution
algorithm.
Specifically,
operator
enables
algorithm
avoid
local
optima;
meanwhile,
strategy
encourages
new
individual
calculate
worst
position.
The
simulation
results
demonstrate
that
can
achieve
minimal
root
mean
square
obtain
better
optima
relative
other
algorithms
in
different
cells.
Therefore,
has
great
potential
used
estimating
model
parameters.
Current Bioinformatics,
Год журнала:
2022,
Номер
18(2), С. 109 - 142
Опубликована: Сен. 20, 2022
Background:
Moth-flame
optimization
will
meet
the
premature
and
stagnation
phenomenon
when
encountering
difficult
tasks.
Objective:
To
overcome
above
shortcomings,
this
paper
presented
a
quasi-reflection
moth-flame
algorithm
with
refraction
learning
called
QRMFO
to
strengthen
property
of
ordinary
MFO
apply
it
in
various
application
fields.
Method:
In
proposed
QRMFO,
quasi-reflection-based
increases
diversity
population
expands
search
space
on
iteration
jump
phase;
improves
accuracy
potential
optimal
solution.
Results:
Several
experiments
are
conducted
evaluate
superiority
paper;
first
all,
CEC2017
benchmark
suite
is
utilized
estimate
capability
dealing
standard
test
sets
compared
state-of-the-art
algorithms;
afterward,
adopted
deal
multilevel
thresholding
image
segmentation
problems
real
medical
diagnosis
case.
Conclusion:
Simulation
results
discussions
show
that
optimizer
superior
basic
other
advanced
methods
terms
convergence
rate
solution
accuracy.
Journal of Computational Design and Engineering,
Год журнала:
2022,
Номер
9(3), С. 1007 - 1044
Опубликована: Апрель 23, 2022
Abstract
The
ant
colony
optimization
algorithm
is
a
classical
swarm
intelligence
algorithm,
but
it
cannot
be
used
for
continuous
class
problems.
A
(ACOR)
proposed
to
overcome
this
difficulty.
Still,
some
problems
exist,
such
as
quickly
falling
into
local
optimum,
slow
convergence
speed,
and
low
accuracy.
To
solve
these
problems,
paper
proposes
modified
version
of
ACOR
called
ADNOLACO.
There
an
opposition-based
learning
mechanism
introduced
effectively
improve
the
speed
ACOR.
All-dimension
neighborhood
also
further
enhance
ability
avoid
getting
trapped
in
optimum.
strongly
demonstrate
core
advantages
ADNOLACO,
with
30
benchmark
functions
IEEE
CEC2017
basis,
detailed
analysis
ADNOLACO
not
only
qualitatively
performed,
comparison
experiment
conducted
between
its
peers.
results
fully
proved
that
has
accelerated
improved
find
balance
globally
optimal
solutions
improved.
Also,
show
practical
value
real
applications,
deals
four
engineering
simulation
illustrate
can
accuracy
computational
results.
Therefore,
demonstrated
promising
excellent
based
on
IET Renewable Power Generation,
Год журнала:
2022,
Номер
16(8), С. 1535 - 1561
Опубликована: Март 25, 2022
Abstract
In
this
paper,
an
efficient
sine
cosine
differential
gradient‐based
optimization
method
is
proposed
for
identifying
unknown
parameters
of
photovoltaic
models.
the
simulation,
parameter
identification
formulated
as
objective
function
to
be
minimized
based
on
error
between
estimated
and
experimental
data.
Based
original
method,
combines
mutation
crossover
evolution
algorithm.
Specifically,
operator
enables
algorithm
avoid
local
optima;
meanwhile,
strategy
encourages
new
individual
calculate
worst
position.
The
simulation
results
demonstrate
that
can
achieve
minimal
root
mean
square
obtain
better
optima
relative
other
algorithms
in
different
cells.
Therefore,
has
great
potential
used
estimating
model
parameters.
