Blood-sucking leech optimizer
Advances in Engineering Software,
Год журнала:
2024,
Номер
195, С. 103696 - 103696
Опубликована: Июнь 15, 2024
Язык: Английский
A multi-objective optimization of porous sandwich functionally graded plates with graphene nanoplatelet reinforcement using Blood-Sucking leech Optimizer
Composite Structures,
Год журнала:
2025,
Номер
unknown, С. 118921 - 118921
Опубликована: Фев. 1, 2025
Язык: Английский
A comprehensive survey of golden jacal optimization and its applications
Computer Science Review,
Год журнала:
2025,
Номер
56, С. 100733 - 100733
Опубликована: Фев. 11, 2025
Язык: Английский
Multi-Strategy Golden Jackal Optimization for engineering design
The Journal of Supercomputing,
Год журнала:
2025,
Номер
81(4)
Опубликована: Март 13, 2025
Язык: Английский
A metaheuristic optimization framework inspired by virus mutations and its ability to optimize the structural design of 2D and 3D steel frames compared to other methods
Results in Engineering,
Год журнала:
2025,
Номер
unknown, С. 105020 - 105020
Опубликована: Апрель 1, 2025
Язык: Английский
An efficient multi-objective algorithm based on Rao and differential evolution for solving bi-objective truss optimization
Engineering Optimization,
Год журнала:
2025,
Номер
unknown, С. 1 - 31
Опубликована: Фев. 24, 2025
Язык: Английский
A novel reward-based golden jackal optimization algorithm uses mix-weighted dynamic and random opposition learning to solve optimization problems
Cluster Computing,
Год журнала:
2025,
Номер
28(5)
Опубликована: Апрель 28, 2025
Язык: Английский
Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System
Energies,
Год журнала:
2024,
Номер
17(15), С. 3738 - 3738
Опубликована: Июль 29, 2024
In
order
to
address
the
issues
of
significant
energy
and
resource
waste,
low-energy
management
efficiency,
high
building-maintenance
costs
in
hot-summer
cold-winter
regions
China,
a
research
project
was
conducted
on
an
office
building
located
Nantong.
this
study,
data-driven
golden
jackal
optimization
(GJO)-based
Long
Short-Term
Memory
(LSTM)
short-term
energy-consumption
prediction
system
is
proposed.
The
creates
equivalent
model
employs
genetic
algorithm
tool
Wallacei
automatically
optimize
control
building’s
air
conditioning
system,
thereby
achieving
objective
reducing
consumption.
To
validate
authenticity
scheme,
unoptimized
consumption
predicted
using
consumption-prediction
model.
actual
comparison
data
confirmed
that
reduction
resulted
from
implementing
conditioning-optimization
scheme
rather
than
external
factors.
optimized
can
achieve
hourly
saving
rate
6%
9%,
with
average
daily
energy-saving
reaching
8%.
entire
therefore,
enables
decision-makers
swiftly
assess
efficacy
consumption-optimization
programs,
furnishing
scientific
foundation
for
real-world
buildings.
Язык: Английский
CGJO: a novel complex-valued encoding golden jackal optimization
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Авг. 23, 2024
Golden
jackal
optimization
(GJO)
is
inspired
by
mundane
characteristics
and
collaborative
hunting
behaviour,
which
mimics
foraging,
trespassing
encompassing,
capturing
prey
to
refresh
a
jackal's
position.
However,
the
GJO
has
several
limitations,
such
as
slow
convergence
rate,
low
computational
accuracy,
premature
convergence,
poor
solution
efficiency,
weak
exploration
exploitation.
To
enhance
global
detection
ability
this
paper
proposes
novel
complex-valued
encoding
golden
(CGJO)
achieve
function
engineering
design.
The
strategy
deploys
dual-diploid
organization
encode
real
imaginary
portions
of
converts
dual-dimensional
region
single-dimensional
manifestation
region,
increases
population
diversity,
restricts
search
stagnation,
expands
area,
promotes
information
exchange,
fosters
collaboration
efficiency
improves
accuracy.
CGJO
not
only
exhibits
strong
adaptability
robustness
supplementary
advantages
but
also
balances
local
exploitation
promote
precision
determine
best
solution.
CEC
2022
test
suite
six
real-world
designs
are
utilized
evaluate
effectiveness
feasibility
CGJO.
compared
with
three
categories
existing
algorithms:
(1)
WO,
HO,
NRBO
BKA
recently
published
algorithms;
(2)
SCSO,
GJO,
RGJO
SGJO
highly
cited
(3)
L-SHADE,
LSHADE-EpsSin
CMA-ES
performing
algorithms.
experimental
results
reveal
that
superior
those
other
superiority
reliability
quicker
greater
computation
precision,
stability
robustness.
Язык: Английский