Water,
Journal Year:
2023,
Volume and Issue:
15(3), P. 486 - 486
Published: Jan. 25, 2023
Modeling
potential
evapotranspiration
(ET0)
is
an
important
issue
for
water
resources
planning
and
management
projects
involving
droughts
flood
hazards.
Evapotranspiration,
one
of
the
main
components
hydrological
cycle,
highly
effective
in
drought
monitoring.
This
study
investigates
efficiency
two
machine-learning
methods,
random
vector
functional
link
(RVFL)
relevance
machine
(RVM),
improved
with
new
metaheuristic
algorithms,
quantum-based
avian
navigation
optimizer
algorithm
(QANA),
artificial
hummingbird
(AHA)
modeling
ET0
using
limited
climatic
data,
minimum
temperature,
maximum
extraterrestrial
radiation.
The
outcomes
hybrid
RVFL-AHA,
RVFL-QANA,
RVM-AHA,
RVM-QANA
models
compared
single
RVFL
RVM
models.
Various
input
combinations
three
data
split
scenarios
were
employed.
results
revealed
that
AHA
QANA
considerably
methods
ET0.
Considering
periodicity
component
radiation
as
inputs
prediction
accuracy
applied
methods.
IEEE Access,
Journal Year:
2022,
Volume and Issue:
10, P. 49445 - 49473
Published: Jan. 1, 2022
In
this
paper,
a
new
bio-inspired
metaheuristic
algorithm
called
Zebra
Optimization
Algorithm
(ZOA)
is
developed;
its
fundamental
inspiration
the
behavior
of
zebras
in
nature.
ZOA
simulates
foraging
and
their
defense
strategy
against
predators'
attacks.
The
steps
are
described
then
mathematically
modeled.
performance
optimization
evaluated
on
sixty-eight
benchmark
functions,
including
unimodal,
high-dimensional
multimodal,
fixed-dimensional
CEC2015,
CEC2017.
results
obtained
from
compared
with
nine
well-known
algorithms.
simulation
show
that
can
solve
problems
by
creating
suitable
balance
between
exploration
exploitation
has
superior
to
competitor
ZOA's
ability
real-world
been
tested
four
engineering
design
problems,
namely,
tension/compression
spring,
welded
beam,
speed
reducer,
pressure
vessel.
an
effective
optimizer
determining
values
variables
these
Computers,
Journal Year:
2021,
Volume and Issue:
10(11), P. 136 - 136
Published: Oct. 25, 2021
Advancements
in
medical
technology
have
created
numerous
large
datasets
including
many
features.
Usually,
all
captured
features
are
not
necessary,
and
there
redundant
irrelevant
features,
which
reduce
the
performance
of
algorithms.
To
tackle
this
challenge,
metaheuristic
algorithms
used
to
select
effective
However,
most
them
scalable
enough
from
as
well
small
ones.
Therefore,
paper,
a
binary
moth-flame
optimization
(B-MFO)
is
proposed
datasets.
Three
categories
B-MFO
were
developed
using
S-shaped,
V-shaped,
U-shaped
transfer
functions
convert
canonical
MFO
continuous
binary.
These
evaluated
on
seven
results
compared
with
four
well-known
algorithms:
BPSO,
bGWO,
BDA,
BSSA.
In
addition,
convergence
behavior
comparative
assessed,
statistically
analyzed
Friedman
test.
The
experimental
demonstrate
superior
solving
feature
selection
problem
for
different
other
