IET Renewable Power Generation,
Год журнала:
2023,
Номер
unknown
Опубликована: Ноя. 3, 2023
Abstract
Here,
a
new
approach
is
proposed
for
solving
the
optimal
power
flow
(OPF)
problem
in
transmission
networks
using
Gradient
Bald
Eagle
Search
Algorithm
(GBES)
with
Local
Escaping
Operator
(LEO).
The
method
takes
into
account
uncertainty
of
renewable
energy
sources
(wind
and
photovoltaic
systems)
Vehicle‐to‐Grid
(V2G)
stochastic
OPF
problem.
To
improve
efficiency
technique
enhance
its
local
exploitation
capability,
LEO
method's
selection
features
are
utilized.
Monte
Carlo
methods
employed
to
estimate
generation
costs
PEVs
study
their
feasibility.
represented
by
Weibull,
lognormal,
normal
probability
distribution
functions
(PDFs).
GBES
experimentally
compared
well‐known
meta‐heuristics
twenty‐three
different
test
functions,
results
indicate
superiority
over
BES
other
recently
developed
algorithms.
Furthermore,
effectiveness
evaluated
IEEE
30‐bus
system
under
various
scenarios,
simulation
demonstrate
that
it
can
effectively
address
issues
considering
V2G,
providing
superior
solutions
Applied Sciences,
Год журнала:
2022,
Номер
12(19), С. 10057 - 10057
Опубликована: Окт. 6, 2022
The
Lemur
Optimizer
(LO)
is
a
novel
nature-inspired
algorithm
we
propose
in
this
paper.
This
algorithm’s
primary
inspirations
are
based
on
two
pillars
of
lemur
behavior:
leap
up
and
dance
hub.
These
principles
mathematically
modeled
the
optimization
context
to
handle
local
search,
exploitation,
exploration
search
concepts.
LO
first
benchmarked
twenty-three
standard
functions.
Additionally,
used
solve
three
real-world
problems
evaluate
its
performance
effectiveness.
In
direction,
compared
six
well-known
algorithms:
Salp
Swarm
Algorithm
(SSA),
Artificial
Bee
Colony
(ABC),
Sine
Cosine
(SCA),
Bat
(BA),
Flower
Pollination
(FPA),
JAYA
algorithm.
findings
show
that
proposed
outperforms
these
algorithms
fourteen
functions
proves
LO’s
robust
managing
exploitation
capabilities,
which
significantly
leads
towards
global
optimum.
experimental
demonstrate
how
may
tackle
such
challenges
competitively.
Journal of Computational Design and Engineering,
Год журнала:
2023,
Номер
10(4), С. 1390 - 1422
Опубликована: Июнь 15, 2023
Abstract
In
2021,
a
meta-heuristic
algorithm,
Reptile
Search
Algorithm
(RSA),
was
proposed.
RSA
mainly
simulates
the
cooperative
predatory
behavior
of
crocodiles.
Although
has
fast
convergence
speed,
due
to
influence
crocodile
predation
mechanism,
if
algorithm
falls
into
local
optimum
in
early
stage,
will
probably
be
unable
jump
out
optimum,
resulting
poor
comprehensive
performance.
Because
shortcomings
RSA,
introducing
escape
operator
can
effectively
improve
crocodiles'
ability
explore
space
and
generate
new
crocodiles
replace
Benefiting
from
adding
restart
strategy,
when
optimal
solution
is
no
longer
updated,
algorithm’s
improved
by
randomly
initializing
crocodile.
Then
joining
Ghost
opposition-based
learning
balance
IRSA’s
exploitation
exploration,
Improved
with
Opposition-based
Learning
for
Global
Optimization
Problem
(IRSA)
To
verify
performance
IRSA,
we
used
nine
famous
optimization
algorithms
compare
IRSA
23
standard
benchmark
functions
CEC2020
test
functions.
The
experiments
show
that
good
robustness,
solve
six
classical
engineering
problems,
thus
proving
its
effectiveness
solving
practical
problems.
