In
order
to
improve
the
dung
beetle
optimization
algorithm
is
prone
fall
into
problem
of
local
optimal.
this
paper,
Manta
ray
spiral
foraging
mechanism
was
integrated
regeneration
stage
rolling
beetles
and
female
breeding
beetles,
so
as
ability
jump
out
optimal
search
speed.
Compared
with
whale
gray
Wolf
algorithm,
six
benchmark
test
functions
are
used
improved
algorithm.
The
experiment
proves
that
has
better
global
convergence
speed
than
other
comparison
algorithms.
feasibility
practicability
in
practical
application
verified.
Decision Analytics Journal,
Год журнала:
2022,
Номер
5, С. 100125 - 100125
Опубликована: Сен. 6, 2022
Firefly
algorithm
(FA)
is
a
powerful
and
efficient
meta-heuristic
which
has
shown
effective
performance
in
the
recent
literature
when
applied
to
solving
engineering
optimization
problems.
FA
imitates
flashing
behavior
of
fireflies.
generates
solutions
randomly
assumes
them
as
However,
these
algorithms
may
suffer
from
premature
convergence
poor
global
exploration
used
optimize
complex
high
dimension
Therefore,
this
study
proposed
novel
FA,
called
firefly
1
3
(FA1→3),
via
different
types
movements
fireflies
an
attempt
improve
characteristics
FA.
A
comprehensive
been
carried
out
on
CEC2014
test
functions
compare
FA1→3
with
standard
several
modern
improved
validate
its
performance.
The
experimental
results
demonstrate
that
achieved
acceptable
In
addition,
it
six
real-world
problems
show
capability,
robustness,
efficacy
comparison
As
per
simulations,
provided
suitable
higher
accuracy
than
traditional
modified
introduced
last
years.
According
significantly
robust
dealing
various
finds
design
variables
straightforwardly.
Note
source
code
publicly
available
at
https://www.optim-app.com/projects/FA.
Biomimetics,
Год журнала:
2025,
Номер
10(2), С. 112 - 112
Опубликована: Фев. 13, 2025
To
balance
the
diversity
and
stringency
of
Pareto
solutions
in
multi-objective
optimization,
this
paper
introduces
a
White
Shark
Optimization
algorithm
(MONSWSO)
tailored
for
optimization.
MONSWSO
integrates
non-dominated
sorting
crowding
distance
into
framework
to
select
optimal
solution
within
population.
The
uniformity
initial
population
is
enhanced
through
chaotic
reverse
initialization
learning
strategy.
adaptive
updating
individual
positions
facilitated
by
an
elite-guided
forgetting
mechanism,
which
incorporates
escape
energy
eddy
aggregation
behavior
inspired
marine
organisms
improve
exploration
key
areas.
evaluate
effectiveness
MONSWSO,
it
benchmarked
against
five
state-of-the-art
algorithms
using
four
metrics:
inverse
generation
distance,
spatial
homogeneity,
distribution,
hypervolume
on
27
typical
problems,
including
23
functions
4
project
examples.
Furthermore,
practical
application
demonstrated
example
optimizing
design
subway
tunnel
foundation
pits.
comprehensive
results
reveal
that
outperforms
comparison
algorithms,
achieving
impressive
satisfactory
outcomes.
Processes,
Год журнала:
2023,
Номер
11(2), С. 498 - 498
Опубликована: Фев. 7, 2023
In
this
paper,
an
improved
gradient-based
optimizer
(IGBO)
is
proposed
with
the
target
of
improving
performance
and
accuracy
algorithm
for
solving
complex
optimization
engineering
problems.
The
IGBO
has
added
features
adjusting
best
solution
by
adding
inertia
weight,
fast
convergence
rate
modified
parameters,
as
well
avoiding
local
optima
using
a
novel
functional
operator
(G).
These
make
it
feasible
majority
nonlinear
problems
which
quite
hard
to
achieve
original
version
GBO.
effectiveness
scalability
are
evaluated
well-known
benchmark
functions.
Moreover,
statistically
analyzed
ANOVA
analysis,
Holm–Bonferroni
test.
addition,
was
assessed
real-world
results
functions
show
that
very
competitive,
superior
compared
its
competitors
in
finding
optimal
solutions
high
coverage.
studied
real
prove
superiority
difficult
indefinite
search
domains.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Окт. 19, 2024
The
Whale
Optimization
Algorithm
(WOA)
is
regarded
as
a
classic
metaheuristic
algorithm,
yet
it
suffers
from
limited
population
diversity,
imbalance
between
exploitation
and
exploration,
low
solution
accuracy.
In
this
paper,
we
propose
the
Spiral-Enhanced
(SEWOA),
which
incorporates
nonlinear
time-varying
self-adaptive
perturbation
strategy
an
Archimedean
spiral
structure
into
original
WOA.
enhances
diversity
of
space,
aiding
algorithm
in
escaping
local
optima.
optimization
dynamic
improves
algorithm's
search
capability
effectiveness
proposed
validated
multiple
perspectives
using
CEC2014
test
functions,
CEC2017
23
benchmark
functions.
experimental
results
demonstrate
that
enhanced
significantly
balances
global
search,
Additionally,
SEWOA
exhibits
excellent
performance
solving
three
engineering
design
problems,
showcasing
its
value
wide
range
potential
applications.
Mathematics,
Год журнала:
2024,
Номер
12(2), С. 243 - 243
Опубликована: Янв. 11, 2024
The
Salp
Swarm
Algorithm
(SSA)
is
a
bio-inspired
metaheuristic
optimization
technique
that
mimics
the
collective
behavior
of
chains
hunting
for
food
in
ocean.
While
it
demonstrates
competitive
performance
on
benchmark
problems,
SSA
faces
challenges
with
slow
convergence
and
getting
trapped
local
optima
like
many
population-based
algorithms.
To
address
these
limitations,
this
study
proposes
locally
weighted
(LWSSA),
which
combines
two
mechanisms
into
standard
framework.
First,
approach
introduced
integrated
to
guide
search
toward
promising
regions.
This
heuristic
iteratively
probes
high-quality
solutions
neighborhood
refines
current
position.
Second,
mutation
operator
generates
new
positions
followers
increase
randomness
throughout
search.
In
order
assess
its
effectiveness,
proposed
was
evaluated
against
state-of-the-art
metaheuristics
using
test
functions
from
IEEE
CEC
2021
2017
competitions.
methodology
also
applied
risk
assessment
cardiovascular
disease
(CVD).
Seven
strategies
extreme
gradient
boosting
(XGBoost)
classifier
are
compared
LWSSA-XGBoost
model.
achieves
superior
prediction
94%
F1
score,
recall,
93%
accuracy,
area
under
ROC
curve
comparison
competitors.
Overall,
experimental
results
demonstrate
LWSSA
enhances
SSA’s
ability
XGBoost
predictive
power
automated
CVD
assessment.
International Journal of Green Energy,
Год журнала:
2024,
Номер
unknown, С. 1 - 18
Опубликована: Окт. 6, 2024
Achieving
optimal
energy
planning
in
Microgrids
(MGs)
is
pivotal
for
addressing
complex
challenges
associated
with
cost-effective
and
reliable
supplies.
This
paper
proposes
a
novel
hybrid
metaheuristic
algorithm
microgrids
using
an
Improved
Salp
Swarm
Algorithm
Harris
Hawk
Foraging
(ISSAHF).
technique
based
on
improved
multi-leader
elite
leader
following
strategy
combined
Hawks
foraging.
A
simulation
study
conducted
low-voltage
microgrid
off-grid
grid-connected
modes.
The
optimization
resulted
daily
average
cost
of
28.3370€
mode
compared
to
19.2676€
one.
Furthermore,
the
statistical
shows
that
proposed
outperforms
well-established
techniques
regarding
search
capability
robustness.
It
yields
mean
623.5248€
404.7475€
one,
other
vary
from
667.2141€
959.5747€
mode,
424.5841€
813.932€
mode.
For
robustness,
performs
well
standard
deviation
20.765€
best
(17.024€)
worst
(47.2423€)
cases
while
it
28.8771€
(21.6316€)
(45.3774€)
values.
