Symmetry,
Journal Year:
2024,
Volume and Issue:
16(10), P. 1255 - 1255
Published: Sept. 24, 2024
An
infinite
impulse
response
(IIR)
system
might
comprise
a
multimodal
error
surface
and
accurately
discovering
the
appropriate
filter
parameters
for
modeling
remains
complicated.
The
swarm
intelligence
algorithms
facilitate
IIR
filter’s
by
exploring
parameter
domains
exploiting
acceptable
sets.
This
paper
presents
an
enhanced
symmetric
sand
cat
optimization
with
multiple
strategies
(MSSCSO)
to
achieve
adaptive
identification.
principal
objective
is
recognize
most
regulating
coefficients
minimize
mean
square
(MSE)
between
unidentified
system’s
input
output.
MSSCSO
cooperative
swarms
integrates
ranking-based
mutation
operator,
elite
opposition-based
learning
strategy,
simplex
method
capture
supplementary
advantages,
disrupt
regional
extreme
solutions,
identify
finest
potential
solutions.
not
only
receives
extensive
exploration
exploitation
refrain
from
precocious
convergence
foster
computational
efficiency;
it
also
endures
robustness
reliability
demographic
variability
elevate
estimation
precision.
experimental
results
manifest
that
practicality
feasibility
of
are
superior
those
other
methods
in
terms
speed,
calculation
precision,
detection
efficiency,
coefficients,
MSE
fitness
value.
Sustainable Cities and Society,
Journal Year:
2024,
Volume and Issue:
114, P. 105721 - 105721
Published: Aug. 3, 2024
Microgrid
cost
management
is
a
significant
difficulty
because
the
energy
generated
by
microgrids
typically
derived
from
variety
of
renewable
and
non-renewable
sources.
Furthermore,
in
order
to
meet
requirements
freed
markets
secure
load
demand,
link
between
microgrid
national
grid
always
preferred.
For
all
these
reasons,
minimize
operating
expenses,
it
imperative
design
smart
unit
regulate
various
resources
inside
microgrid.
In
this
study,
idea
for
multi-source
operation
presented.
The
proposed
utilizes
Improved
Artificial
Rabbits
Optimization
Algorithm
(IAROA)
which
used
optimize
based
on
current
prices
generation
capacities.
Also,
comparison
optimization
outcomes
obtained
results
implemented
using
Honey
Badger
(HBA),
Whale
(WOA).
prove
applicability
feasibility
method
demand
system
SMG.
price
after
applying
HBA
6244.5783
(ID).
But
Algorithm,
found
4283.9755
(ID),
1227.4482
By
comparing
with
conventional
method,
whale
algorithm
saved
31.396
%
per
day,
artificial
rabbit's
80.3437
day.
From
gives
superior
performance.
Alexandria Engineering Journal,
Journal Year:
2024,
Volume and Issue:
98, P. 364 - 389
Published: May 11, 2024
There
are
many
classic
highly
complex
optimization
problems
in
the
world,
therefore,
it
is
still
necessary
to
find
an
applicable
and
effective
algorithm
solve
these
problems.
In
this
paper,
self-adaptive
hybrid
cross
mutation
slime
mold
proposed,
which
AHCSMA,
efficiently.
Specifically,
there
three
innovations
paper:
(i)
new
Cauchy
operator
developed
improve
ability
of
population;
(ii)
crossover
rate
balance
mechanism
proposed
make
up
for
neglected
relationship
between
individuals
rates.
Then
differential
vector
information
dominant
individual
other
population
utilized
increase
evolution
speed
algorithm;
(iii)
restart
opposition
learning
designed
alleviate
situation
where
falls
into
local
optimality.
To
verify
competitive
UAV
path
planning
problems,
engineering
nonlinear
parameter
extraction
photovoltaic
model
identification
infinite
impulse
response
used
test
accumulation
more
than
50
algorithms
as
comparison
algorithms,
results
report
that
AHCSMA
extremely
performs
better
when
optimizing
real-life
AIMS Mathematics,
Journal Year:
2024,
Volume and Issue:
9(5), P. 13410 - 13438
Published: Jan. 1, 2024
<abstract>
<p>In
this
study,
we
present
a
comprehensive
framework
for
enhancing
the
temperature
control
of
electric
furnaces,
integrating
three
novel
components:
proportional-integral-derivative
controller
with
filter
(PID-F),
customized
objective
function,
and
modified
eel
foraging
optimization
(mEEFO)
algorithm.
The
PID-F
controller,
introduced
first
time
in
literature
leverages
coefficient
to
effectively
mitigate
kick
effect,
improving
transient
frequency
responses.
To
further
optimize
employed
mEEFO,
recently
proposed
metaheuristic
inspired
by
social
predation
behaviors
eels,
tailored
modifications
furnace
control.
study
also
introduces
new
based
on
modification
integral
absolute
error
(IAE)
performance
index.
was
evaluated
through
extensive
comparisons
established
algorithms,
including
statistical
analysis,
Wilcoxon
signed-rank
test,
domain
analyses.
Comparative
assessments
reported
methods,
such
as
genetic
algorithms
Ziegler–Nichols-based
PID
controllers,
validated
efficacy
approach,
highlighting
its
transformative
impact
regulation.
non-ideal
conditions
measurement
noise,
external
disturbance,
saturation
at
output
were
order
demonstrate
superior
approach
from
wider
perspective.
Furthermore,
robustness
against
variations
system
parameters
demonstrated.</p>
</abstract>
Optimal Control Applications and Methods,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 24, 2025
ABSTRACT
This
paper
introduces
the
modified
dandelion
optimizer
(mDO),
a
novel
adaptive
metaheuristic
algorithm
designed
to
address
complex
engineering
optimization
challenges,
with
focus
on
infinite
impulse
response
(IIR)
system
identification.
The
proposed
mDO
incorporates
three
key
advancements:
an
enhanced
descending
phase
improve
global
exploration,
exploration‐exploitation
that
balances
search
intensity
and
breadth,
self‐adaptive
crossover
operator
refines
solutions
dynamically.
These
innovations
specifically
target
challenges
associated
high‐order
IIR
modeling,
enabling
deliver
more
precise
efficient
To
validate
its
performance,
was
rigorously
evaluated
across
diverse
testing
environments,
including
CEC2017
CEC2022
benchmark
functions,
various
model
identification
scenarios,
real‐world
design
problems
such
as
multi‐product
batch
plant
design,
multiple
disk
clutch
brake
speed
reducer
design.
Comparative
analyses
reveal
consistently
outperforms
leading
algorithms
in
terms
of
accuracy,
robustness,
computational
efficiency,
particularly
complex,
high‐dimensional
landscapes.
Statistical
assessments
further
confirm
mDO's
superior
capability
accurately
identifying
parameters
even
under
noise
varying
orders.
study
positions
competitive
versatile
tool
for
applications,
offering
significant
improvements
accuracy
adaptability
advanced
modeling
problem‐solving.
