MORIME: A Multi-Objective RIME Optimization Framework for Efficient Truss Design
Results in Engineering,
Год журнала:
2025,
Номер
25, С. 103933 - 103933
Опубликована: Янв. 5, 2025
Язык: Английский
A new enhanced grey wolf optimizer to improve geospatially subsurface analyses
Modeling Earth Systems and Environment,
Год журнала:
2025,
Номер
11(2)
Опубликована: Янв. 31, 2025
Язык: Английский
Reshaping Industrial Maintenance with Machine Learning: Fouling Control Using Optimized Gaussian Process Regression
Industrial & Engineering Chemistry Research,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 14, 2025
Язык: Английский
Optimizing N-1 Contingency Rankings Using a Nature-Inspired Modified Sine Cosine Algorithm
IIUM Engineering Journal,
Год журнала:
2025,
Номер
26(1), С. 398 - 419
Опубликована: Янв. 10, 2025
Ensuring
the
reliability
and
sustainability
of
power
systems
is
essential
for
maintaining
efficient
uninterrupted
operations,
especially
under
varying
load
conditions
potential
faults.
This
study
tackles
critical
task
contingency
ranking
by
evaluating
severity
disturbances
caused
transmission
line
disconnections.
Such
evaluations
enable
system
operators
to
make
informed
strategic
decisions
during
real-time
scenarios.
A
novel
approach
utilizing
Modified
Sine
Cosine
Algorithm
(MSCA),
a
nature-inspired
metaheuristic
optimization
technique,
proposed
resolve
(N-1)
rankings
efficiently.
The
MSCA
method
validated
using
IEEE
30-bus
test
case,
focusing
on
optimal
parameter
tuning
population
size,
iterations,
key
variables.
Results
demonstrate
that
achieves
high
capture
ratio
96.67%,
explores
only
8.33
×
10??%
search
space,
requires
processing
time
3.69
seconds.
Compared
with
established
methods
such
as
Ant
Colony
Optimization
(ACO)
Genetic
(GA),
exhibits
superior
computational
efficiency
while
competitive
accuracy.
These
findings
underline
in
applications
where
speed
precision
are
critical.
By
closely
matching
manual
rankings,
integrates
assessment
techniques,
offering
practical
value
improving
resilience
reducing
risks
associated
disruptions.
research
advances
state-of-the-art
approaches,
providing
planners
robust
tool
addressing
complex
challenges.
ABSTRAK:
Memastikan
keandalan
dan
kelestarian
sistem
tenaga
elektrik
adalah
penting
untuk
mengekalkan
operasi
yang
cekap
tidak
terganggu,
terutamanya
dalam
menghadapi
keadaan
beban
berubah-ubah
kemungkinan
kerosakan.
Kajian
ini
menangani
tugas
kritikal
perangkingan
kontingensi
dengan
menilai
tahap
keparahan
gangguan
disebabkan
oleh
pemutusan
talian
penghantaran.
Penilaian
sebegini
membolehkan
pengendali
membuat
keputusan
berinformasi
strategik
senario
masa
nyata.
Pendekatan
baharu
menggunakan
satu
teknik
pengoptimuman
metaheuristik
diilhamkan
alam,
dicadangkan
menyelesaikan
cekap.
Kaedah
disahkan
kes
ujian
memberi
tumpuan
kepada
penalaan
optimum
saiz
populasi,
iterasi,
pemboleh
ubah
utama.
Keputusan
menunjukkan
bahawa
mencapai
nisbah
tangkapan
tinggi
sebanyak
hanya
meneroka
daripada
ruang
pencarian,
memerlukan
pemprosesan
saat.
Berbanding
kaedah
sedia
ada
seperti
kecekapan
pengiraan
unggul
sambil
ketepatan
kompetitif.
Penemuan
menekankan
potensi
aplikasi
nyata
di
mana
kelajuan
kritikal.
Dengan
hasil
hampir
menyamai
manual,
mengintegrasikan
penilaian
pengoptimuman,
memberikan
nilai
praktikal
meningkatkan
daya
tahan
mengurangkan
risiko
berkaitan
gangguan.
Penyelidikan
memajukan
pendekatan
terkini
menyediakan
perancang
alat
kukuh
cabaran
kompleks.
Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement
Elsevier eBooks,
Год журнала:
2025,
Номер
unknown, С. 197 - 228
Опубликована: Янв. 1, 2025
Язык: Английский
Gaussian combined arms algorithm: a novel meta-heuristic approach for solving engineering problems
Evolutionary Intelligence,
Год журнала:
2025,
Номер
18(2)
Опубликована: Март 18, 2025
Язык: Английский
Cloud Drift Optimization (CDO) Algorithm: A Nature-Inspired Metaheuristic
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 14, 2025
Abstract
This
study
introduces
the
Cloud
Drift
Optimization
(CDO)
algorithm,
an
innovative
nature-inspired
metaheuristic
approach
to
solving
complex
optimization
problems.
The
CDO
algorithm
mimics
dynamic
behavior
of
cloud
particles
influenced
by
atmospheric
forces,
striking
a
refined
balance
between
exploration
and
exploitation.
It
features
adaptive
weight
adjustment
mechanism
that
alters
cloud's
drift
in
real-time,
allowing
for
efficient
navigation
through
search
space.
Using
cloud-based
strategy,
harnesses
probabilistic
movements
maneuver
landscape
more
effectively.
has
undergone
rigorous
testing
against
various
established
unimodal
multimodal
benchmark
functions,
where
it
showcases
outstanding
performance
characterized
faster
convergence
rates,
high
robustness,
exceptional
solution
accuracy
compared
top
contemporary
techniques.
Additionally,
applies
numerous
real-world
engineering
tasks,
such
as
designing
cantilever
beams,
three-bar
trusses,
tension/compression
springs,
pressure
vessels.
empirical
data
highlight
CDO's
ability
deliver
solutions
across
fields,
machine
learning
applications,
other
practical
scenarios.
These
results
indicate
is
promising
tool
tackling
highly
multidimensional
problems
academic
industrial
environments.
Язык: Английский
Optimal distributed generation placement and sizing using modified grey wolf optimization and ETAP for power system performance enhancement and protection adaptation
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 22, 2025
Язык: Английский
An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering
Array,
Год журнала:
2025,
Номер
unknown, С. 100409 - 100409
Опубликована: Май 1, 2025
Язык: Английский
Artificial Optimizer Algorithm for Power System Stabilizer design problem and multidisciplinary engineering applications
Heliyon,
Год журнала:
2024,
Номер
10(22), С. e40068 - e40068
Опубликована: Ноя. 1, 2024
The
novelty
of
this
research
lies
in
presenting
a
fresh
stochastic
algorithm
enthused
via
'Velociraptor'
social
intelligence
wildlife
(or
nature),
known
as
the
Velociraptor
Group
Optimization
(VROA).
In
strategy,
co-operative
natural
life
cycle
is
mathematically
framed,
and
novel
mechanisms
are
presented
to
perform
search
exploration)
hunting
exploitation).
This
suggests
that
proposed
method
reveals
noteworthy
ability
both
exploitation
exploration.
Furthermore,
it
successfully
stabilities
exploration
exploitation,
supporting
process.
direction
evaluating
VROA,
we
utilized
on
51
CEC'17,
CEC'20,
CEC'22
standard
benchmark
suites
six
multidisciplinary
engineering
optimization
functions.
well-known
statistical
methods
like
Wilcoxon
rank-sum
test
Friedman's
have
been
used
verify
strength
against
various
optimizers.
tabulated
numerical
solutions
show
VROA
performs
better
than
recent
optimizers
most
has
efficiently
attained
competent
resolution
while
concurrently
upholding
adherence
designated
constraints.
results
validate
can
offer
efficient
accurate
optimal
evaluation
with
metaheuristics.
Язык: Английский