Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Oct. 29, 2024
In
this
paper,
a
novel
hybrid
sine-cosine
and
spotted
Hyena-based
chimp
optimization
algorithm
(hybrid
SSC)
is
adopted
for
the
precise
tuning
of
proportional-integral
(PI)
controllers
in
microgrid
system.
The
integrates
multiple
renewable
energy
sources,
including
photovoltaic
(PV)
panels,
wind
turbines,
fuel
cell,
battery
storage
system,
all
connected
to
common
DC
bus.
This
bus
interfaces
with
main
grid
through
voltage
source
converter
(VSC).
comprises
total
eight
PI
distributed
across
various
components:
boost
cell
device,
VSC
controller.
SSC
effectively
combines
exploration
capabilities
(SCA)
exploitation
strengths
Hyena
optimizer
(SHO)
Chimp
(ChOA),
aiming
achieve
optimal
controllers.
approach
ensures
an
enhanced
dynamic
response
overall
system
performance
by
minimizing
integral
time-weighted
squared
error
(ITSE)
each
simulation
results,
directed
MATLAB/SIMULINK
environment,
demonstrate
efficacy
improving
stability,
time
microgrid.
proposed
technique
significantly
outperforms
traditional
techniques,
ensuring
robust
operation
seamless
addition
sources
grid.
study
contributes
advancement
intelligent
control
strategies
modern
microgrids,
emphasizing
importance
algorithms
achieving
complex
systems.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: April 18, 2024
Sand
cat
swarm
optimization
algorithm
is
a
meta-heuristic
created
to
replicate
the
hunting
behavior
observed
by
sand
cats.
The
presented
method
(CWXSCSO)
addresses
issues
of
low
convergence
precision
and
local
optimality
in
standard
algorithm.
It
accomplished
this
through
utilization
elite
decentralization
crossbar
approach.
To
begin
with,
novel
dynamic
exponential
factor
introduced.
Furthermore,
throughout
developmental
phase,
approach
incorporated
augment
capacity
transcend
confines
optimal.
Ultimately,
crossover
technique
employed
produce
solutions
algorithm's
emerge
from
space.
techniques
were
evaluated
performing
comparison
with
15
benchmark
functions.
CWXSCSO
was
compared
six
advanced
upgraded
algorithms
using
CEC2019
CEC2021.
Statistical
analysis,
complexity
analysis
use
statistics
for
assessing
it.
verify
its
efficacy
solving
engineering
difficulties
handling
traditional
problems.
results
demonstrate
that
exhibits
higher
global
capability
demonstrates
proficiency
dealing
real-world
applications.
Informatics in Medicine Unlocked,
Journal Year:
2024,
Volume and Issue:
46, P. 101467 - 101467
Published: Jan. 1, 2024
The
optimization
of
the
vaccination
campaign
and
medication
distribution
in
rural
regions
Morocco
conducted
by
Ministry
Health
can
be
significantly
improved
employing
metaheuristic
algorithms
conjunction
with
a
tour
planning
system.
This
research
proposes
utilization
six
algorithms:
genetic
algorithm,
rat
swarm
optimization,
whale
spotted
hyena
optimizer,
penguins
search
particle
to
determine
most
efficient
routes
for
equipped
trucks
carrying
vaccines
medications.
These
consider
critical
field
constraints,
such
as
operating
hours
centers,
vaccine
availability,
distances
between
centers
while
minimizing
overall
journey
duration.
In
addition,
comprehensive
system
is
integrated
into
framework
accounting
transportation
costs
fuel
expenses
truck
maintenance
costs.
By
incorporating
these
factors,
aims
achieve
maximum
efficiency
financial
burden
associated
areas.
integration
metaheuristics
presents
robust
data-driven
solution
enhance
effectiveness
their
campaigns
Morocco.
approach
not
only
minimizes
but
also
improves
ensuring
timely
access
medications
population.
findings
this
contribute
growing
body
knowledge
healthcare
logistics
provide
valuable
insights
policymakers
practitioners
involved
similar
worldwide.
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(19), P. 8624 - 8624
Published: Sept. 25, 2024
To
tackle
the
shortcomings
of
Dung
Beetle
Optimization
(DBO)
Algorithm,
which
include
slow
convergence
speed,
an
imbalance
between
exploration
and
exploitation,
susceptibility
to
local
optima,
a
Somersault
Foraging
Elite
Opposition-Based
Learning
(SFEDBO)
Algorithm
is
proposed.
This
algorithm
utilizes
elite
opposition-based
learning
strategy
as
method
for
generating
initial
population,
resulting
in
more
diverse
population.
address
exploitation
algorithm,
adaptive
employed
dynamically
adjust
number
dung
beetles
eggs
with
each
iteration
Inspired
by
Manta
Ray
(MRFO)
we
utilize
its
somersault
foraging
perturb
position
optimal
individual,
thereby
enhancing
algorithm’s
ability
escape
from
optima.
verify
effectiveness
proposed
improvements,
SFEDBO
utilized
optimize
23
benchmark
test
functions.
The
results
show
that
achieves
better
solution
accuracy
stability,
outperforming
DBO
terms
optimization
on
Finally,
was
applied
practical
application
problems
pressure
vessel
design,
tension/extension
spring
3D
unmanned
aerial
vehicle
(UAV)
path
planning,
were
obtained.
research
shows
this
paper
applicable
actual
has
performance.
Applied System Innovation,
Journal Year:
2023,
Volume and Issue:
6(5), P. 80 - 80
Published: Sept. 4, 2023
In
this
paper,
we
propose
a
novel
methodology
that
combines
the
opposition
Nelder–Mead
algorithm
and
selection
phase
of
genetic
algorithm.
This
integration
aims
to
enhance
performance
overall
To
evaluate
effectiveness
our
methodology,
conducted
comprehensive
comparative
study
involving
11
state-of-the-art
algorithms
renowned
for
their
exceptional
in
2022
IEEE
Congress
on
Evolutionary
Computation
(CEC
2022).
Following
rigorous
analysis,
which
included
Friedman
test
subsequent
Dunn’s
post
hoc
test,
demonstrated
outstanding
performance.
fact,
exhibited
equal
or
superior
compared
other
majority
cases
examined.
These
results
highlight
competitiveness
proposed
approach,
showcasing
its
potential
achieve
solving
optimization
problems.
