Engineering Optimization,
Journal Year:
2025,
Volume and Issue:
unknown, P. 1 - 22
Published: Jan. 15, 2025
In
view
of
the
problem
high
energy
consumption
and
control
costs
caused
by
uneven
airflow
distribution
unreasonable
in
complex
mine
ventilation
networks,
this
study
takes
minimum
number
regulators
as
optimization
objectives
to
establish
a
multi-objective
model
for
networks.
Based
on
roadway
adjustable
attributes
spanning
tree
principle,
location
was
reasonably
determined.
Moreover,
article
proposes
an
improved
invasive
weed
(IIWO)
algorithm
solve
with
coupling
nonlinearity.
Compared
other
algorithms,
IIWO
showed
excellent
performance.
applied
optimize
network.
The
results
show
that
can
effectively
reduce
network,
saving
rate
fan
is
31.78%.
Applied Soft Computing,
Journal Year:
2023,
Volume and Issue:
148, P. 110908 - 110908
Published: Oct. 11, 2023
Activity-based
scheduling
optimization
is
a
combinatorial
problem
built
on
the
traveling
salesman
intending
to
optimize
people
schedules
considering
their
trips
and
available
transportation
network.
Due
difficulty
of
scheduling,
traditional
exact
methods
are
unable
provide
appropriate
solutions.
Hence,
new
approaches
have
been
introduced
in
literature
settle
these
complex
problems.
One
group
techniques
known
as
metaheuristic
algorithms,
which
provides
robust
family
problem-solving
created
by
mimicking
natural
phenomena.
Although
might
not
find
an
optimal
solution,
they
can
near-optimal
one
moderate
period.
Furthermore,
myriad
novel
algorithms
has
making
it
tedious
for
academics
select
technique.
Thus,
this
paper
investigates
contribution
metaheuristics
solve
transportation-related
To
achieve
aim,
we
conducted
bibliometric
analysis,
defined
descriptive
assessment
features
120
metaheuristics.
The
findings
study
reveal
usage
tendencies
identify
most
prevalent
ones,
highlight
those
that
potential
use
upcoming
research.
results
demonstrate
applied
algorithm
genetic
algorithm,
but
ant
colony
popular
based
number
citations.
Lastly,
open
discussion
few
unexplored
research
gaps
expectations.
Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
57(3)
Published: Feb. 12, 2024
Abstract
This
paper
proposes
a
novel
nature-inspired
swarm-based
optimization
algorithm
called
elk
herd
optimizer
(EHO).
It
is
inspired
by
the
breeding
process
of
herd.
Elks
have
two
main
seasons:
rutting
and
calving.
In
season,
splits
into
different
families
various
sizes.
division
based
on
fighting
for
dominance
between
bulls,
where
stronger
bull
can
form
family
with
large
numbers
harems.
calving
each
breeds
new
calves
from
its
inspiration
set
in
an
context
loop
consists
three
operators:
selection
season.
During
all
are
merged,
including
harems,
calves.
The
fittest
will
be
selected
use
upcoming
seasons.
simple
words,
EHO
divides
population
groups,
one
leader
several
followers
number
determined
fitness
value
group.
Each
group
generate
solutions
members
groups
leaders,
followers,
combined
performance
assessed
using
29
benchmark
problems
utilized
CEC-2017
special
sessions
real-parameter
four
traditional
real-world
engineering
design
problems.
comparative
results
were
conducted
against
ten
well-established
metaheuristic
algorithms
showed
that
proposed
yielded
best
almost
functions
used.
Statistical
testing
Friedman’s
test
post-hocked
Holm’s
function
confirms
superiority
when
compared
to
other
methods.
nutshell,
efficient
used
tackle
Journal Of Big Data,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: Aug. 4, 2024
Abstract
As
the
number
and
cleverness
of
cyber-attacks
keep
increasing
rapidly,
it's
more
important
than
ever
to
have
good
ways
detect
prevent
them.
Recognizing
cyber
threats
quickly
accurately
is
crucial
because
they
can
cause
severe
damage
individuals
businesses.
This
paper
takes
a
close
look
at
how
we
use
artificial
intelligence
(AI),
including
machine
learning
(ML)
deep
(DL),
alongside
metaheuristic
algorithms
better.
We've
thoroughly
examined
over
sixty
recent
studies
measure
effective
these
AI
tools
are
identifying
fighting
wide
range
threats.
Our
research
includes
diverse
array
cyberattacks
such
as
malware
attacks,
network
intrusions,
spam,
others,
showing
that
ML
DL
methods,
together
with
algorithms,
significantly
improve
well
find
respond
We
compare
methods
out
what
they're
where
could
improve,
especially
face
new
changing
cyber-attacks.
presents
straightforward
framework
for
assessing
Methods
in
threat
detection.
Given
complexity
threats,
enhancing
regularly
ensuring
strong
protection
critical.
evaluate
effectiveness
limitations
current
proposed
models,
addition
algorithms.
vital
guiding
future
enhancements.
We're
pushing
smart
flexible
solutions
adapt
challenges.
The
findings
from
our
suggest
protecting
against
will
rely
on
continuously
updating
stay
ahead
hackers'
latest
tricks.
Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
57(8)
Published: July 4, 2024
Abstract
Rapid
industrialization
has
fueled
the
need
for
effective
optimization
solutions,
which
led
to
widespread
use
of
meta-heuristic
algorithms.
Among
repertoire
over
600,
300
new
methodologies
have
been
developed
in
last
ten
years.
This
increase
highlights
a
sophisticated
grasp
these
novel
methods.
The
biological
and
natural
phenomena
inform
strategies
seen
paradigm
shift
recent
observed
trend
indicates
an
increasing
acknowledgement
effectiveness
bio-inspired
tackling
intricate
engineering
problems,
providing
solutions
that
exhibit
rapid
convergence
rates
unmatched
fitness
scores.
study
thoroughly
examines
latest
advancements
optimisation
techniques.
work
investigates
each
method’s
unique
characteristics,
properties,
operational
paradigms
determine
how
revolutionary
approaches
could
be
problem-solving
paradigms.
Additionally,
extensive
comparative
analyses
against
conventional
benchmarks,
such
as
metrics
search
history,
trajectory
plots,
functions,
are
conducted
elucidate
superiority
approaches.
Our
findings
demonstrate
potential
optimizers
provide
directions
future
research
refine
expand
upon
intriguing
methodologies.
survey
lighthouse,
guiding
scientists
towards
innovative
rooted
various
mechanisms.
Biomimetics,
Journal Year:
2024,
Volume and Issue:
9(5), P. 291 - 291
Published: May 13, 2024
The
dung
beetle
optimization
(DBO)
algorithm,
a
swarm
intelligence-based
metaheuristic,
is
renowned
for
its
robust
capability
and
fast
convergence
speed.
However,
it
also
suffers
from
low
population
diversity,
susceptibility
to
local
optima
solutions,
unsatisfactory
speed
when
facing
complex
problems.
In
response,
this
paper
proposes
the
multi-strategy
improved
algorithm
(MDBO).
core
improvements
include
using
Latin
hypercube
sampling
better
initialization
introduction
of
novel
differential
variation
strategy,
termed
"Mean
Differential
Variation",
enhance
algorithm's
ability
evade
optima.
Moreover,
strategy
combining
lens
imaging
reverse
learning
dimension-by-dimension
was
proposed
applied
current
optimal
solution.
Through
comprehensive
performance
testing
on
standard
benchmark
functions
CEC2017
CEC2020,
MDBO
demonstrates
superior
in
terms
accuracy,
stability,
compared
with
other
classical
metaheuristic
algorithms.
Additionally,
efficacy
addressing
real-world
engineering
problems
validated
through
three
representative
application
scenarios
namely
extension/compression
spring
design
problems,
reducer
welded
beam
Artificial Intelligence Review,
Journal Year:
2025,
Volume and Issue:
58(3)
Published: Jan. 6, 2025
Spotted
Hyena
Optimizer
(SHO)
is
a
population-based
metaheuristic
algorithm
inspired
by
the
spotted
hyenas'
social
behavior,
and
it
has
been
developed
to
solve
global
optimization
problems.
SHO
shown
superior
performance
over
its
competitive
algorithms
in
solving
benchmark
function
engineering
design
However,
suffers
from
getting
stuck
local
optima
due
lack
of
exploration
while
multi-modal
This
article
proposes
an
improved
SHO,
quantum
(QSHO),
computing.
The
QSHO
implements
computing
mechanism
promote
ability.
novel
method
tested
on
well-known
IEEE
CEC2013
CEC2017
suits
with
30
50
dimensions
four
real-world
results
are
compared
that
Classical
(ISHO),
Modified
(MSHO),
Oppositional
mutation
operator
(OBL-MO-SHO),
space
transformation
search
(STS-SHO),
Quantum
Salp
Swarm
Algorithm
(QSSA),
Chimp
Optimization
(ChOA).
analyzed
using
Wilcoxon
Signed
Rank
Test
(WSRT)
Friedman
Test.
empirical
show
statistically
outperforms
other
for
problem
dimensions.
According
statistics,
ranked
first
second
30D
50D,
respectively,
whereas
both
50D.
In
addition,
we
have
assessed
problems,
algorithms.
Journal Of Big Data,
Journal Year:
2025,
Volume and Issue:
12(1)
Published: Jan. 14, 2025
Abstract
The
rapid
increase
of
fraud
attacks
on
banking
systems,
financial
institutions,
and
even
credit
card
holders
demonstrate
the
high
demand
for
enhanced
detection
(FD)
systems
these
attacks.
This
paper
provides
a
systematic
review
techniques
using
Artificial
Intelligence
(AI),
machine
learning
(ML),
deep
(DL),
meta-heuristic
optimization
(MHO)
algorithms
(CCFD).
Carefully
selected
recent
research
papers
have
been
investigated
to
examine
effectiveness
AI-integrated
approaches
in
recognizing
wide
range
These
AI
were
evaluated
compared
discover
advantages
disadvantages
each
one,
leading
exploration
existing
limitations
ML
or
DL-enhanced
models.
Discovering
limitation
is
crucial
future
work
robustness
various
key
finding
from
this
study
demonstrates
need
continuous
development
models
that
could
be
alert
latest
fraudulent
activities.
