Scientific Reports,
Journal Year:
2023,
Volume and Issue:
13(1)
Published: Dec. 21, 2023
The
grey
wolf
optimizer
is
an
effective
and
well-known
meta-heuristic
algorithm,
but
it
also
has
the
weaknesses
of
insufficient
population
diversity,
falling
into
local
optimal
solutions
easily,
unsatisfactory
convergence
speed.
Therefore,
we
propose
a
hybrid
(HGWO),
based
mainly
on
exploitation
phase
harris
hawk
optimization.
It
includes
initialization
with
Latin
hypercube
sampling,
nonlinear
factor
perturbations,
some
extended
exploration
strategies.
In
HGWO,
wolves
can
have
hawks-like
flight
capabilities
during
position
updates,
which
greatly
expands
search
range
improves
global
searchability.
By
incorporating
greedy
will
relocate
only
if
new
location
superior
to
current
one.
This
paper
assesses
performance
(HGWO)
by
comparing
other
heuristic
algorithms
enhanced
schemes
optimizer.
evaluation
conducted
using
23
classical
benchmark
test
functions
CEC2020.
experimental
results
reveal
that
HGWO
algorithm
performs
well
in
terms
its
ability,
speed,
accuracy.
Additionally,
demonstrates
considerable
advantages
solving
engineering
problems,
thus
substantiating
effectiveness
applicability.
PLoS ONE,
Journal Year:
2023,
Volume and Issue:
18(1), P. e0280006 - e0280006
Published: Jan. 3, 2023
Monkey
king
evolution
(MKE)
is
a
population-based
differential
evolutionary
algorithm
in
which
the
single
strategy
and
control
parameter
affect
convergence
balance
between
exploration
exploitation.
Since
strategies
have
considerable
impact
on
performance
of
algorithms,
collaborating
multiple
can
significantly
enhance
abilities
algorithms.
This
our
motivation
to
propose
multi-trial
vector-based
monkey
named
MMKE.
It
introduces
novel
best-history
trial
vector
producer
(BTVP)
random
(RTVP)
that
effectively
collaborate
with
canonical
MKE
(MKE-TVP)
using
approach
tackle
various
real-world
optimization
problems
diverse
challenges.
expected
proposed
MMKE
improve
global
search
capability,
strike
exploitation,
prevent
original
from
converging
prematurely
during
process.
The
was
assessed
CEC
2018
test
functions,
results
were
compared
eight
metaheuristic
As
result
experiments,
it
demonstrated
capable
producing
competitive
superior
terms
accuracy
rate
comparison
comparative
Additionally,
Friedman
used
examine
gained
experimental
statistically,
proving
Furthermore,
four
engineering
design
optimal
power
flow
(OPF)
problem
for
IEEE
30-bus
system
are
optimized
demonstrate
MMKE's
real
applicability.
showed
handle
difficulties
associated
able
solve
multi-objective
OPF
better
solutions
than
Electronics,
Journal Year:
2022,
Volume and Issue:
11(5), P. 831 - 831
Published: March 7, 2022
The
optimal
power
flow
(OPF)
is
a
practical
problem
in
system
with
complex
characteristics
such
as
large
number
of
control
parameters
and
also
multi-modal
non-convex
objective
functions
inequality
nonlinear
constraints.
Thus,
tackling
the
OPF
becoming
major
priority
for
engineers
researchers.
Many
metaheuristic
algorithms
different
search
strategies
have
been
developed
to
solve
problem.
Although,
majority
them
suffer
from
stagnation,
premature
convergence,
local
optima
trapping
during
optimization
process,
which
results
producing
low
solution
qualities,
especially
real-world
problems.
This
study
devoted
proposing
an
effective
hybridizing
whale
algorithm
(WOA)
modified
moth-flame
(MFO)
named
WMFO
In
proposed
WMFO,
WOA
MFO
cooperate
effectively
discover
promising
areas
provide
high-quality
solutions.
A
randomized
boundary
handling
used
return
solutions
that
violated
permissible
boundaries
space.
Moreover,
greedy
selection
operator
defined
assess
acceptance
criteria
new
Ultimately,
performance
scrutinized
on
single
multi-objective
cases
problems
including
standard
IEEE
14-bus,
30-bus,
39-bus,
57-bus,
IEEE118-bus
test
systems.
obtained
corroborate
outperforms
contender
solving
Electronics,
Journal Year:
2022,
Volume and Issue:
11(12), P. 1919 - 1919
Published: June 20, 2022
The
Harris
hawk
optimizer
is
a
recent
population-based
metaheuristics
algorithm
that
simulates
the
hunting
behavior
of
hawks.
This
swarm-based
performs
optimization
procedure
using
novel
way
exploration
and
exploitation
multiphases
search.
In
this
review
research,
we
focused
on
applications
developments
well-established
robust
(HHO)
as
one
most
popular
techniques
2020.
Moreover,
several
experiments
were
carried
out
to
prove
powerfulness
effectivness
HHO
compared
with
nine
other
state-of-art
algorithms
Congress
Evolutionary
Computation
(CEC2005)
CEC2017.
literature
paper
includes
deep
insight
about
possible
future
directions
ideas
worth
investigations
regarding
new
variants
its
widespread
applications.
Mathematics,
Journal Year:
2022,
Volume and Issue:
10(11), P. 1929 - 1929
Published: June 4, 2022
Medical
technological
advancements
have
led
to
the
creation
of
various
large
datasets
with
numerous
attributes.
The
presence
redundant
and
irrelevant
features
in
negatively
influences
algorithms
leads
decreases
performance
algorithms.
Using
effective
data
mining
analyzing
tasks
such
as
classification
can
increase
accuracy
results
relevant
decisions
made
by
decision-makers
using
them.
This
become
more
acute
when
dealing
challenging,
large-scale
problems
medical
applications.
Nature-inspired
metaheuristics
show
superior
finding
optimal
feature
subsets
literature.
As
a
seminal
attempt,
wrapper
selection
approach
is
presented
on
basis
newly
proposed
Aquila
optimizer
(AO)
this
work.
In
regard,
uses
AO
search
algorithm
order
discover
most
subset.
S-shaped
binary
(SBAO)
V-shaped
(VBAO)
are
two
suggested
for
datasets.
Binary
position
vectors
generated
utilizing
S-
transfer
functions
while
space
stays
continuous.
compared
six
recent
optimization
seven
benchmark
comparison
comparative
algorithms,
gained
demonstrate
that
both
BAO
variants
improve
these
also
tested
real-dataset
COVID-19.
findings
testified
SBAO
outperforms
regarding
least
number
selected
highest
accuracy.
Mathematics,
Journal Year:
2022,
Volume and Issue:
10(10), P. 1696 - 1696
Published: May 16, 2022
Remora
Optimization
Algorithm
(ROA)
is
a
recent
population-based
algorithm
that
mimics
the
intelligent
traveler
behavior
of
Remora.
However,
performance
ROA
barely
satisfactory;
it
may
be
stuck
in
local
optimal
regions
or
has
slow
convergence,
especially
high
dimensional
complicated
problems.
To
overcome
these
limitations,
this
paper
develops
an
improved
version
called
Enhanced
(EROA)
using
three
different
techniques:
adaptive
dynamic
probability,
SFO
with
Levy
flight,
and
restart
strategy.
The
EROA
tested
two
benchmarks
seven
real-world
engineering
statistical
analysis
experimental
results
show
efficiency
EROA.
Computer Modeling in Engineering & Sciences,
Journal Year:
2022,
Volume and Issue:
135(3), P. 1981 - 2006
Published: Sept. 15, 2022
Travelling
Salesman
Problem
(TSP)
is
a
discrete
hybrid
optimization
problem
considered
NP-hard.
TSP
aims
to
discover
the
shortest
Hamilton
route
that
visits
each
city
precisely
once
and
then
returns
starting
point,
making
it
feasible.
This
paper
employed
Farmland
Fertility
Algorithm
(FFA)
inspired
by
agricultural
land
fertility
hyper-heuristic
technique
based
on
Modified
Choice
Function
(MCF).
The
neighborhood
search
operator
can
use
this
strategy
automatically
select
best
heuristic
method
for
decision.
Lin-Kernighan
(LK)
local
has
been
incorporated
increase
efficiency
performance
of
suggested
approach.
71
TSPLIB
datasets
have
compared
with
different
algorithms
prove
proposed
algorithm's
efficiency.
Simulation
results
indicated
algorithm
outperforms
comparable
methods
average
mean
computation
time,
percentage
deviation
(PDav),
tour
length.
