International journal of intelligent engineering and systems,
Journal Year:
2024,
Volume and Issue:
17(3), P. 276 - 289
Published: May 3, 2024
This
paper
introduces
a
novel
metaheuristic
named
the
stochastic
shaking
algorithm
(SSA),
which
is
rooted
in
swarm
intelligence
principles.The
innovation
lies
its
unique
utilization
of
iteration
for
selecting
references
during
guided
searches
through
approach.The
optimization
process
involves
two
sequential
steps:
primary
reference
first
step
finest
member,
while
second
step,
it
mean
all
finer
members
plus
one.This
then
combined
with
randomly
chosen
solution
within
space,
serving
as
secondary
reference.SSA
undergoes
evaluation
contexts.The
assessing
performance
using
set
23
classic
functions
theoretical
use
case.The
tackling
economic
load
dispatch
problem
(ELD),
practical
case
featuring
system
13
generators
various
energy
resources.The
study
compares
SSA
against
five
other
metaheuristics-One
to
One
Based
Optimization
(OOBO),
Kookaburra
Algorithm
(KOA),
Language
Education
(LEO),
Total
Interaction
(TIA),
and
Walrus
(WaOA).Results
indicate
SSA's
superiority
over
OOBO,
KOA,
LEO,
TIA,
WaOA
21,
13,
11,
16,
14
out
functions,
respectively.Additionally,
reveals
intense
competition
among
six
metaheuristics.
Biomimetics,
Journal Year:
2024,
Volume and Issue:
9(2), P. 91 - 91
Published: Feb. 1, 2024
The
present
study
introduces
a
novel
nature-inspired
optimizer
called
the
Pine
Cone
Optimization
algorithm
(PCOA)
for
solving
science
and
engineering
problems.
PCOA
is
designed
based
on
different
mechanisms
of
pine
tree
reproduction,
including
pollination
cone
dispersal
by
gravity
animals.
It
employs
new
powerful
operators
to
simulate
mentioned
mechanisms.
performance
analyzed
using
classic
benchmark
functions,
CEC017
CEC2019
as
mathematical
problems
CEC2006
CEC2011
design
In
terms
accuracy,
results
show
superiority
well-known
algorithms
(PSO,
DE,
WOA)
(AVOA,
RW_GWO,
HHO,
GBO).
are
competitive
with
state-of-the-art
(LSHADE
EBOwithCMAR).
convergence
speed
time
complexity,
reasonable.
According
Friedman
test,
PCOA’s
rank
1.68
9.42
percent
better
than
EBOwithCMAR
(second-best
algorithm)
LSHADE
(third-best
algorithm),
respectively.
authors
recommend
science,
engineering,
industrial
societies
complex
optimization
Processes,
Journal Year:
2025,
Volume and Issue:
13(4), P. 949 - 949
Published: March 23, 2025
This
study
develops
a
novel
Lyrebird
Optimization
Algorithm
(LOA),
technique
inspired
by
the
wild
behavioral
strategies
of
lyrebirds
in
response
to
potential
threats.
In
two-area
interconnected
power
system
that
includes
non-reheat
thermal
stations,
this
algorithm
is
applied
handle
load
frequency
control
(LFC)
optimizing
parameters
Proportional–Integral–Derivative
controller
with
filter
(PIDn).
incorporates
generation
rate
constraints
(GRCs).
The
efficiency
provided
LOA-PIDn
evaluated
through
simulations
under
various
disturbance
scenarios
and
compared
against
other
well-established
optimization
techniques,
including
Ziegler–Nichols
(ZN),
genetic
(GA),
Bacteria
Foraging
(BFOA),
Firefly
Approach
(FA),
hybridized
FA
pattern
search
(hFA–PS),
self-adaptive
multi-population
elitist
Jaya
(SAMPE-Jaya)-based
PI/PID
controllers,
Teaching–Learning-Based
Optimizer
(TLBO)
IDD/PIDD
controllers.
results
demonstrate
LOA’s
ability
minimize
integral
time
multiplied
absolute
error
(ITAE)
achieve
significantly
lower
settling
times
for
frequencies
transferred
variances
comparison
methods.
comprehensive
inclusion
real-world
validate
LOA
as
robust
effective
tool
addressing
complex
challenges
modern
systems.
Optimal Control Applications and Methods,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 8, 2025
ABSTRACT
Lithium‐ion
Battery
State
of
Health
(SOH)
estimation
is
essential
for
reliability
and
safety.
Rechargeable
lithium‐ion
availability
decreased
by
thickening
the
solid
electrolyte
interphase
(SEI)
layer
due
to
interactions
between
electrodes
electrolytes
during
cycles
discharging
charging.
This
paper
proposes
a
hybrid
approach,
called
LOA‐FENN,
reduce
errors
fusing
fully
Elman
neural
network
(FENN)
with
lyrebird
optimization
algorithm
(LOA).
The
LOA
optimizes
network's
weight
training,
while
FENN
predicts
SOH.
Implemented
in
MATLAB,
LOA‐FENN
approach
achieved
an
error
1.08%,
outperforming
existing
methods
such
as
genetic
(HGA),
box‐cox
transformation
(BCT),
particle
filter
(PFA),
which
showed
1.38%,
1.28%,
1.18%,
respectively.
