A
new
meta-heuristic
algorithm
that
has
demonstrated
strong
performance
on
optimization
problems
is
called
the
Snake
Optimizer
(SO).
Nevertheless,
compared
to
other
methods,
SO
a
number
of
drawbacks,
such
as
slow
convergence,
narrow
search
solution
space,
and
easy
settle
into
local
optimal
solutions.
To
address
these
issues,
this
work
proposes
an
improved
snake
optimizer
(CSO)
introduces
chaotic
(CLS)
procedure.
The
goal
implementing
take
advantage
chaos's
traversal
non-repetitive
properties
broaden
population's
diversity
enhance
algorithmic
performance.
In
study,
we
embedded
ten
mappings
process
tested
effectiveness
CSO
23
benchmark
functions
with
different
characteristics
CEC2022
function
set.
Furthermore,
evaluate
CSO's
against
six
competitive
methods
traditional
algorithm.
outcomes
demonstrate
issue,
Improved
appropriate
mapping
performs
better
than
regular
its
rivals.
Computational Intelligence and Neuroscience,
Journal Year:
2022,
Volume and Issue:
2022, P. 1 - 20
Published: Oct. 10, 2022
The
reptile
search
algorithm
(RSA)
is
a
swarm-based
metaheuristic
inspired
by
the
encirclement
and
hunt
mechanisms
of
crocodiles.
Compared
with
other
algorithms,
RSA
competitive
but
still
suffers
from
low
population
diversity,
unbalanced
exploitation
exploration,
tendency
to
fall
into
local
optima.
To
overcome
these
shortcomings,
modified
variant
RSA,
named
MRSA,
proposed
in
this
paper.
First,
an
adaptive
chaotic
reverse
learning
strategy
employed
enhance
diversity.
Second,
elite
alternative
pooling
balance
exploration.
Finally,
shifted
distribution
estimation
used
correct
evolutionary
direction
improve
performance.
Subsequently,
superiority
MRSA
verified
using
23
benchmark
functions,
IEEE
CEC2017
robot
path
planning
problems.
Friedman
test,
Wilcoxon
signed-rank
simulation
results
show
that
outperforms
comparative
algorithms
terms
convergence
accuracy,
speed,
stability.
IEEE Access,
Journal Year:
2022,
Volume and Issue:
10, P. 50384 - 50402
Published: Jan. 1, 2022
Evolutionary
algorithms
have
show
great
successes
in
various
real-world
applications
ranging
molecule
to
astronomy.
As
a
mainstream
evolutionary
algorithm,
differential
evolution
(DE)
possesses
the
characteristics
of
simple
algorithmic
structure,
easy
implement,
and
efficient
search
performance.
Nevertheless,
it
still
suffers
from
issues
local
optimal
trapping
premature
problems.
In
this
study,
we
innovatively
improve
performance
DE
by
incorporating
full
utilization
information
feedback,
which
includes
population's
holistic
direction
vectors.
The
proposed
permutation-archive
directed
(PAIDDE)
algorithm
is
verified
on
set
29
benchmark
numerical
functions
22
optimization
Extensive
experimental
statistical
results
that
PAIDDE
can
significantly
outperform
other
12
state-of-the-art
terms
solution
qualities.
Additionally,
computational
complexity,
distribution,
convergence
speed,
dynamics,
population
diversity
are
systematically
analyzed.
source
code
be
found
at
https://toyamaailab.github.io/sourcedata.html.
International Journal of Low-Carbon Technologies,
Journal Year:
2023,
Volume and Issue:
18, P. 354 - 366
Published: Jan. 1, 2023
Abstract
Cold
chain
logistics
distribution
orders
have
increased
due
to
the
impact
of
COVID-19.
In
view
increasing
difficulty
route
optimization
and
increase
carbon
emissions
in
process
cold
distribution,
a
mathematical
model
for
vehicles
with
minimum
comprehensive
cost
is
established
by
considering
emission
intensity
comprehensively
this
paper.
The
main
contributions
paper
are
as
follows:
1)
An
improved
hybrid
ant
colony
algorithm
proposed,
which
combined
simulated
annealing
get
rid
local
optimal
solution.
2)
Chaotic
mapping
introduced
pheromone
update
accelerate
convergence
improve
search
efficiency.
effectiveness
proposed
method
optimizing
path
reducing
costs
verified
simulation
experiments
comparison
existing
classical
algorithms.
PeerJ Computer Science,
Journal Year:
2023,
Volume and Issue:
9, P. e1526 - e1526
Published: Aug. 22, 2023
Metaheuristic
optimization
algorithms
manage
the
search
process
to
explore
domains
efficiently
and
are
used
in
large-scale,
complex
problems.
Transient
Search
Algorithm
(TSO)
is
a
recently
proposed
physics-based
metaheuristic
method
inspired
by
transient
behavior
of
switched
electrical
circuits
containing
storage
elements
such
as
inductance
capacitance.
TSO
still
new
method;
it
tends
get
stuck
with
local
optimal
solutions
offers
low
precision
sluggish
convergence
rate.
In
order
improve
performance
methods,
different
approaches
can
be
integrated
methods
hybridized
achieve
faster
high
accuracy
balancing
exploitation
exploration
stages.
Chaotic
maps
effectively
escaping
optimum
increasing
this
study,
chaotic
included
accelerate
global
convergence.
prevent
slow
rate
classical
algorithm
from
getting
solutions,
10
that
generate
values
instead
random
processes
for
first
time.
Thus,
ergodicity
non-repeatability
improved,
speed
increased.
The
(CTSO)
was
investigated
using
IEEE
Congress
on
Evolutionary
Computation
(CEC)'17
benchmarking
functions.
Its
real-world
engineering
problems
reducer,
tension
compression
spring,
welded
beam
design,
pressure
vessel,
three-bar
truss
design
addition,
CTSO
feature
selection
evaluated
University
California,
Irvine
(UCI)
standard
datasets.
results
simulation
showed
Gaussian
Sinusoidal
most
comparison
functions,
map
problems,
finally
generally
CTSOs
outperform
other
competitive
methods.
Real
application
demonstrate
suggested
approach
more
effective
than
TSO.
