Journal Of Big Data,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: Jan. 2, 2024
Abstract
Beluga
Whale
Optimization
(BWO)
is
a
new
metaheuristic
algorithm
that
simulates
the
social
behaviors
of
beluga
whales
swimming,
foraging,
and
whale
falling.
Compared
with
other
optimization
algorithms,
BWO
shows
certain
advantages
in
solving
unimodal
multimodal
problems.
However,
convergence
speed
performance
still
have
some
deficiencies
when
complex
multidimensional
Therefore,
this
paper
proposes
hybrid
method
called
HBWO
combining
Quasi-oppositional
based
learning
(QOBL),
adaptive
spiral
predation
strategy,
Nelder-Mead
simplex
search
(NM).
Firstly,
initialization
phase,
QOBL
strategy
introduced.
This
reconstructs
initial
spatial
position
population
by
pairwise
comparisons
to
obtain
more
prosperous
higher
quality
population.
Subsequently,
an
designed
exploration
exploitation
phases.
The
first
learns
optimal
individual
positions
dimensions
through
avoid
loss
local
optimality.
At
same
time,
movement
motivated
cosine
factor
introduced
maintain
balance
between
exploitation.
Finally,
NM
added.
It
corrects
multiple
scaling
methods
improve
accurately
efficiently.
verified
utilizing
CEC2017
CEC2019
test
functions.
Meanwhile,
superiority
six
engineering
design
examples.
experimental
results
show
has
feasibility
effectiveness
practical
problems
than
methods.
Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
57(3)
Published: Feb. 16, 2024
Abstract
This
paper
introduces
HLOA,
a
novel
metaheuristic
optimization
algorithm
that
mathematically
mimics
crypsis,
skin
darkening
or
lightening,
blood-squirting,
and
move-to-escape
defense
methods.
In
crypsis
behavior,
the
lizard
changes
its
color
by
becoming
translucent
to
avoid
detection
predators.
The
horned
can
lighten
darken
skin,
depending
on
whether
not
it
needs
decrease
increase
solar
thermal
gain.
lightening
strategy
is
modeled
including
stimulating
hormone
melanophore
rate(
$$\alpha$$
α
-MHS)
influences
these
changes.
Further,
move-to-evasion
also
described.
lizard’s
shooting
blood
mechanism,
described
as
projectile
motion,
modeled.
These
strategies
balance
exploitation
exploration
mechanisms
for
local
global
search
over
solution
space.
HLOA
performance
benchmarked
with
sixty-three
problems
from
literature,
testbench
provided
in
IEEE
CEC-
2017
“Constrained
Real-Parameter
Optimization”,
analyzed
dimensions
10,
30,
50,
100,
well
functions
CEC-06
2019
“100-Digit
Challenge”.
Moreover,
three
real-world
constraint
applications
CEC2020
two
engineering
problems,
multiple
gravity
assist
optimal
power
flow
problem,
are
studied.
Wilcoxon
Friedman
statistics
tests
compare
results
against
ten
recent
bio-inspired
algorithms.
shows
provides
most
more
effectively
than
competing
At
same
time,
test
ranks
first,
n-dimensional
analysis
performs
better
constrained
50
100.
source
code
free
available
https://www.mathworks.com/matlabcentral/fileexchange/159658-horned-lizard-optimization-algorithm-hloa
.
Journal of Hydrology Regional Studies,
Journal Year:
2024,
Volume and Issue:
52, P. 101703 - 101703
Published: Feb. 12, 2024
A
pilot
case
study
in
East
El
Oweinat
(PCSEO),
Egypt.
An
artificial
neural
network
(ANN)-based
mountain
gazelle
optimization
(MGO)
model
was
applied
to
map
groundwater
potential
zones
(GWPZs).
For
this
purpose,
ten
layers
affecting
occurrence
were
prepared
and
normalized
against
the
drawdown
(DD)
map.
All
data
divided
into
70:30
for
training
testing.
After
that,
sensitivity
analysis
adopted
verify
relative
importance
(RI)
of
layers.
The
accuracy
GWPZs
checked
using
receiver
operating
characteristic
(ROC)
curve
other
statistical
indicators.
finally
propose
a
sustainable
strategy
exploration
by
implementing
integrated
MODFLOW-USG
MGO
framework.
Over
40%
PCSEO
revealed
high
very
degrees
situated
mostly
on
southwestern
side.
Sensitivity
that
significantly
affected
table
(GWT),
well
density
(WD),
land
use
(LU).
results
also
indicated
ANN-based
performed
with
an
area
under
(AUC)
∼
90%
compared
conventional
models.
Additionally,
MODFLOW-USG-based
gave
spatial
distribution
optimal
discharge
well-depth
zones.
This
finding
could
match
SDGs
relevant
ending
poverty,
affordable
groundwater,
life
land.
Journal Of Big Data,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: Jan. 2, 2024
Abstract
Beluga
Whale
Optimization
(BWO)
is
a
new
metaheuristic
algorithm
that
simulates
the
social
behaviors
of
beluga
whales
swimming,
foraging,
and
whale
falling.
Compared
with
other
optimization
algorithms,
BWO
shows
certain
advantages
in
solving
unimodal
multimodal
problems.
However,
convergence
speed
performance
still
have
some
deficiencies
when
complex
multidimensional
Therefore,
this
paper
proposes
hybrid
method
called
HBWO
combining
Quasi-oppositional
based
learning
(QOBL),
adaptive
spiral
predation
strategy,
Nelder-Mead
simplex
search
(NM).
Firstly,
initialization
phase,
QOBL
strategy
introduced.
This
reconstructs
initial
spatial
position
population
by
pairwise
comparisons
to
obtain
more
prosperous
higher
quality
population.
Subsequently,
an
designed
exploration
exploitation
phases.
The
first
learns
optimal
individual
positions
dimensions
through
avoid
loss
local
optimality.
At
same
time,
movement
motivated
cosine
factor
introduced
maintain
balance
between
exploitation.
Finally,
NM
added.
It
corrects
multiple
scaling
methods
improve
accurately
efficiently.
verified
utilizing
CEC2017
CEC2019
test
functions.
Meanwhile,
superiority
six
engineering
design
examples.
experimental
results
show
has
feasibility
effectiveness
practical
problems
than
methods.
