In
this
study,
a
novel
optimization
algorithm,
namely
the
Beluga
Whale
Optimization
Algorithm
(BWO)
with
Loss
sensitivity
factor
(LSF)
is
employed
to
determine
optimal
locations
and
sizes
of
capacitors
in
radial
distribution
system.
The
objective
enhance
voltage
profile
curtail
active
power
losses
by
strategically
placing
at
suitable
determining
their
sizes.
Through
extensive
simulations,
performance
BWO
evaluated
contrasted
other
conventional
metaheuristic
methods.
results
demonstrate
effectiveness
all
algorithms
enhancing
reducing
losses.
Nonetheless,
demonstrates
faster
convergence
higher
solution
quality
comparison.
algorithm
intelligently
explores
space,
adapting
concepts
bubble-net
feeding
echolocation
beluga
whales.
This
enables
converge
efficiently
towards
global
optimum,
outperforming
methods
most
scenarios.
We
have
opted
utilize
two
IEEE
test
bus
systems,
33
69,
implement
suggested
technique.
These
systems
been
specifically
designed
closely
replicate
real-world
network
scenarios,
thus
ensuring
accurate
testing
evaluation.
Journal of Computational Design and Engineering,
Journal Year:
2023,
Volume and Issue:
10(6), P. 2065 - 2093
Published: Oct. 5, 2023
Abstract
The
beluga
whale
optimization
(BWO)
algorithm
is
a
recently
proposed
metaheuristic
that
simulates
three
behaviors:
whales
interacting
in
pairs
to
perform
mirror
swimming,
population
sharing
information
cooperate
predation,
and
fall.
However,
the
performance
of
BWO
still
needs
be
improved
enhance
its
practicality.
This
paper
proposes
modified
(MBWO)
with
multi-strategy.
It
was
inspired
by
whales’
two
group
gathering
for
foraging
searching
new
habitats
long-distance
migration.
aggregation
strategy
(GAs)
migration
(Ms).
GAs
can
improve
local
development
ability
accelerate
overall
rate
convergence
through
fine
search;
Ms
randomly
moves
towards
periphery
population,
enhancing
jump
out
optima.
In
order
verify
MBWO,
this
article
conducted
comprehensive
testing
on
MBWO
using
23
benchmark
functions,
IEEE
CEC2014,
CEC2021.
experimental
results
indicate
has
strong
ability.
also
tests
MBWO’s
solve
practical
engineering
problems
five
problems.
final
prove
effectiveness
solving
IEEE Transactions on Industrial Informatics,
Journal Year:
2024,
Volume and Issue:
20(4), P. 6802 - 6813
Published: Jan. 24, 2024
Composite
insulators
are
prone
to
accelerated
aging
in
coastal
and
industrially
polluted
environments,
leading
flashovers,
power
grid
outages,
economic
losses.
Traditional
detection
methods
either
require
shutdown
or
lack
adequate
evaluation
capabilities.
This
research
introduces
a
pixel-level
assessment
of
the
status
composite
using
hyperspectral
imaging
(HSI)
technology.
And
proposed
least
squares
support
vector
machine
(LSSVM)
based
on
Improved
Beluga
Whale
Optimization
(IBWO)
algorithm
evaluate
aged
levels
insulators.
First,
artificial
samples
categorized
into
I-VI
static
contact
angle.
Hyperspectral
data
400
nm–1040
nm
wavelength
range
acquired
HSI
device,
followed
by
preprocessing
steps
involving
denoising
dimensionality
reduction.
Subsequently,
IBWO
is
employed
identify
optimal
parameters
(
C
,
xmlns:xlink="http://www.w3.org/1999/xlink">σ
)
for
LSSVM.
The
performance
was
compared
with
other
optimization
algorithms
test
functions,
demonstrating
that
exhibits
convergence
speed
accuracy,
effectively
enhancing
classification
generalization
ability
In
this
study,
method
k-fold
cross
validation,
resulting
overall
accuracy
96.83%
its
superior
capability.
presented
enables
degree
visualizes
distribution
states
It
provides
valuable
guidance
studying
characteristics
structural
design
diverse
complex
holding
significant
potential
noncontact
electrical
equipment.
