Journal of low frequency noise, vibration and active control,
Journal Year:
2023,
Volume and Issue:
43(2), P. 956 - 978
Published: Nov. 18, 2023
This
paper
presents
a
novel
hybrid
algorithm
based
on
CMOGWO-ADNSGA-II
to
solve
the
vibration
stability
problem
during
operation
of
EMS-type
maglev
train
dynamics
model
subjected
strong
non-linear
magnetic
buoyancy.
The
proposed
optimizes
control
system
parameters
suspensions
by
combining
an
improved
multi-objective
chaotic
grey
wolf
(CMOGWO)
with
non-dominated
Sorting
genetic
algorithm-II
(ADNSGA-II)
enhance
search
capability
and
ensure
population
diversity.
efficacy
is
demonstrated
applying
it
suspension
frame
find
optimal
parameters.
Experimental
results
show
that
applied
significantly
reduces
gap
amplitude
corresponding
standard
deviation,
as
well
vertical
acceleration
deviation
operation.
provides
good
solution
for
control,
which
can
improve
its
performance
safety.
Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
57(4)
Published: March 23, 2024
Abstract
This
paper
innovatively
proposes
the
Black
Kite
Algorithm
(BKA),
a
meta-heuristic
optimization
algorithm
inspired
by
migratory
and
predatory
behavior
of
black
kite.
The
BKA
integrates
Cauchy
mutation
strategy
Leader
to
enhance
global
search
capability
convergence
speed
algorithm.
novel
combination
achieves
good
balance
between
exploring
solutions
utilizing
local
information.
Against
standard
test
function
sets
CEC-2022
CEC-2017,
as
well
other
complex
functions,
attained
best
performance
in
66.7,
72.4
77.8%
cases,
respectively.
effectiveness
is
validated
through
detailed
analysis
statistical
comparisons.
Moreover,
its
application
solving
five
practical
engineering
design
problems
demonstrates
potential
addressing
constrained
challenges
real
world
indicates
that
it
has
significant
competitive
strength
comparison
with
existing
techniques.
In
summary,
proven
value
advantages
variety
due
excellent
performance.
source
code
publicly
available
at
https://www.mathworks.com/matlabcentral/fileexchange/161401-black-winged-kite-algorithm-bka
.
Alexandria Engineering Journal,
Journal Year:
2023,
Volume and Issue:
82, P. 358 - 376
Published: Oct. 14, 2023
The
0–1
Knapsack
problem
is
a
non-deterministic
polynomial-time-hard
combinatorial
optimization
that
cannot
be
solved
in
reasonable
time
using
traditional
methods.
Therefore,
researchers
have
turned
to
metaheuristic
algorithms
for
their
ability
solve
several
problems
amount
of
time.
This
paper
adapts
the
Kepler
algorithm
eight
V-shaped
and
S-shaped
transfer
functions
create
binary
variant
called
BKOA
solving
problem.
Several
experiments
were
conducted
compare
efficacy
competing
optimizers
when
20
well-known
knapsack
instances
with
dimensions
ranging
from
4
75.
experimental
results
demonstrate
superiority
this
over
other
algorithms,
except
genetic
algorithm,
which
marginally
superior.
To
further
improve
it
combined
an
enhanced
improvement
strategy
new
hybrid
variant.
variant,
termed
HBKOA,
has
superior
exploration
exploitation
capabilities
make
better
than
all
performance
metrics
considered.
also
integrated
optimizers,
show
manta
ray
foraging
optimization,
equilibrium
optimizer
are
competitive
small
medium-dimensional
higher
dimensions.
Journal Of Big Data,
Journal Year:
2025,
Volume and Issue:
12(1)
Published: Jan. 28, 2025
Abstract
This
paper
presents
a
binary
variant
of
the
recently
proposed
spider
wasp
optimizer
(SWO),
namely
BSWO,
for
accurately
tackling
multidimensional
knapsack
problem
(MKP),
which
is
classified
as
an
NP-hard
optimization
problem.
The
classical
methods
could
not
achieve
acceptable
results
this
in
reasonable
amount
time.
Therefore,
researchers
have
turned
their
focus
to
metaheuristic
algorithms
address
more
and
However,
majority
MKP
suffer
from
slow
convergence
speed
low
quality
final
results,
especially
number
dimensions
increases.
motivates
us
present
BSWO
discretized
using
nine
well-known
transfer
functions
belonging
three
categories—X-shaped,
S-shaped,
V-shaped
families—for
effectively
efficiently
In
addition,
it
integrated
with
improved
repair
operator
4
(RO4)
hybrid
variant,
BSWO-RO4,
improve
infeasible
solutions
achieving
better
performance.
Several
small,
medium,
large-scale
instances
are
used
assess
both
BSWO-RO4.
usefulness
efficiency
also
demonstrated
by
comparing
them
several
optimizers
terms
some
performance
criteria.
experimental
findings
demonstrate
that
BSWO-RO4
can
exceptional
small
medium-scale
instances,
while
genetic
algorithm
RO4
be
superior
instances.
Additionally,
experiments
efficient
than
RO2.
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(10), P. 3986 - 3986
Published: May 8, 2024
The
coal
loading
operation
of
the
preparation
plant
an
open
pit
mine
causes
chaos
in
vehicle
scheduling
due
to
unreasonable
arrival
times
outgoing
and
container
transportation
vehicles.
To
further
reduce
length
time
that
equipment
waits
for
each
other
total
cost
transportation,
optimisation
model
is
constructed
minimise
minimum
sum
shortest
reversal
lowest
transportation.
accurately
measure
backward
waiting
unit
parameters
are
introduced,
measured
using
transformation
method.
An
improved
grey
wolf
algorithm
proposed
speed
up
convergence
improve
quality
solution.
When
employing
genetic
(GA)
(GWO)
optimising
transport
vehicles
mines,
it
noted
while
GA
can
achieve
global
optimum,
its
relatively
slow.
Conversely,
GWO
converges
more
quickly,
but
tends
be
trapped
local
optima.
accelerate
solution
quality,
a
hybrid
GA−GWO
proposed,
which
introduces
three
operations
selection,
crossover,
mutation
into
prevent
from
falling
optimum
fall;
at
same
time,
hunting
attacking
elite
retention
strategy
GA,
improves
stability
algorithm’s
convergence.
Analysis
indicates
that,
compared
SA,
GWO,
enhances
by
43.1%,
46.2%,
43.7%,
increases
accuracy
4.12%,
6.1%,
3.2%,
respectively,
reduces
35.46,
22,
31
h.
reduced
2437
RMB,
3512
1334
respectively.
