Frontiers in artificial intelligence and applications,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Jan. 12, 2024
A
brand-new
swarm
intelligence
optimization
algorithm
called
the
Sparrow
Search
Algorithm
(SSA)
was
put
forth
in
2020.
By
simulating
foraging
process
of
sparrows,
SSA
efficiently
solves
problems,
exhibiting
advantages
such
as
faster
convergence
and
excellent
abilities.
This
paper
introduces
basic
principles
Algorithm,
analyzes
its
existing
issues,
summarizes
improvements
made
to
algorithms,
then
discusses
application
deep
learning.
Axioms,
Journal Year:
2023,
Volume and Issue:
12(8), P. 767 - 767
Published: Aug. 7, 2023
To
solve
the
problems
of
original
sparrow
search
algorithm’s
poor
ability
to
jump
out
local
extremes
and
its
insufficient
achieve
global
optimization,
this
paper
simulates
different
learning
forms
students
in
each
ranking
segment
class
proposes
a
customized
method
(CLSSA)
based
on
multi-role
thinking.
Firstly,
cube
chaos
mapping
is
introduced
initialization
stage
increase
inherent
randomness
rationality
distribution.
Then,
an
improved
spiral
predation
mechanism
proposed
for
acquiring
better
exploitation.
Moreover,
strategy
designed
after
follower
phase
balance
exploration
A
boundary
processing
full
utilization
important
location
information
used
improve
processing.
The
CLSSA
tested
21
benchmark
optimization
problems,
robustness
verified
12
high-dimensional
functions.
In
addition,
comprehensive
capability
further
proven
CEC2017
test
functions,
intuitive
given
by
Friedman's
statistical
results.
Finally,
three
engineering
are
utilized
verify
effectiveness
solving
practical
problems.
comparative
analysis
shows
that
can
significantly
quality
solution
be
considered
excellent
SSA
variant.
Remote Sensing,
Journal Year:
2023,
Volume and Issue:
15(17), P. 4238 - 4238
Published: Aug. 29, 2023
The
trophic
state
is
an
important
factor
reflecting
the
health
of
lake
ecosystems.
To
accurately
assess
large
lakes,
integrated
framework
was
developed
by
combining
remote
sensing
data,
field
monitoring
machine
learning
algorithms,
and
optimization
algorithms.
First,
key
meteorological
environmental
factors
from
in
situ
were
combined
with
remotely
sensed
reflectance
data
statistical
analysis
used
to
determine
main
influencing
state.
Second,
a
index
(TSI)
inversion
model
constructed
using
algorithm,
this
then
optimized
sparrow
search
algorithm
(SSA)
based
on
backpropagation
neural
network
(BP-NN)
establish
SSA-BP-NN
model.
Third,
typical
China
(Hongze
Lake)
chosen
as
case
study.
application
results
show
that,
when
(pH,
temperature,
average
wind
speed,
sediment
content)
band
combination
Sentinel-2/MSI
input
variables,
performance
improved
(R2
=
0.936,
RMSE
1.133,
MAPE
1.660%,
MAD
0.604).
Compared
prior
0.834,
1.790,
2.679%,
1.030),
accuracy
12.2%.
It
worth
noting
that
could
identify
water
bodies
different
states.
Finally,
framework,
we
mapped
spatial
distribution
TSI
Hongze
Lake
seasons
2019
2020
analyzed
its
variation
characteristics.
can
combine
regional
special
feature
influenced
complex
environment
S-2/MSI
achieve
assessment
over
90%
for
sensitive
waters
has
strong
applicability
robustness.
Agronomy,
Journal Year:
2023,
Volume and Issue:
13(10), P. 2464 - 2464
Published: Sept. 23, 2023
Precision
irrigation
and
fertilization
in
agriculture
are
vital
for
sustainable
crop
production,
relying
on
accurate
determination
of
the
crop’s
nutritional
status.
However,
there
challenges
optimizing
traditional
neural
networks
to
achieve
this
accurately.
This
paper
aims
propose
a
rapid
identification
method
water
nitrogen
content
using
optimized
networks.
addresses
difficulty
backpropagation
network
(BPNN)
structure.
It
uses
179
multi−spectral
images
crops
(such
as
maize)
samples
model.
Particle
swarm
optimization
(PSO)
is
applied
optimize
hidden
layer
nodes.
Additionally,
proposes
double−hidden−layer
structure
improve
model’s
prediction
accuracy.
The
proposed
PSO−BPNN
model
showed
9.87%
improvement
accuracy
compared
with
BPNN
correlation
coefficient
R2
predicted
was
0.9045
0.8734,
respectively.
experimental
results
demonstrate
high
training
efficiency
lays
strong
foundation
developing
precision
plans
modern
holds
promising
prospects.
Frontiers in artificial intelligence and applications,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Jan. 12, 2024
A
brand-new
swarm
intelligence
optimization
algorithm
called
the
Sparrow
Search
Algorithm
(SSA)
was
put
forth
in
2020.
By
simulating
foraging
process
of
sparrows,
SSA
efficiently
solves
problems,
exhibiting
advantages
such
as
faster
convergence
and
excellent
abilities.
This
paper
introduces
basic
principles
Algorithm,
analyzes
its
existing
issues,
summarizes
improvements
made
to
algorithms,
then
discusses
application
deep
learning.
