PLoS ONE,
Journal Year:
2024,
Volume and Issue:
19(12), P. e0314391 - e0314391
Published: Dec. 20, 2024
In
the
contemporary
context
of
a
burgeoning
energy
crisis,
accurate
and
dependable
prediction
Solar
Radiation
(SR)
has
emerged
as
an
indispensable
component
within
thermal
systems
to
facilitate
renewable
generation.
Machine
Learning
(ML)
models
have
gained
widespread
recognition
for
their
precision
computational
efficiency
in
addressing
SR
challenges.
Consequently,
this
paper
introduces
innovative
model,
denoted
Cheetah
Optimizer-Random
Forest
(CO-RF)
model.
The
CO
plays
pivotal
role
selecting
most
informative
features
hourly
forecasting,
subsequently
serving
inputs
RF
efficacy
developed
CO-RF
model
is
rigorously
assessed
using
two
publicly
available
datasets.
Evaluation
metrics
encompassing
Mean
Absolute
Error
(MAE),
Squared
(MSE),
coefficient
determination
(
R
2
)
are
employed
validate
its
performance.
Quantitative
analysis
demonstrates
that
surpasses
other
techniques,
Logistic
Regression
(LR),
Support
Vector
(SVM),
Artificial
Neural
Network,
standalone
Random
(RF),
both
training
testing
phases
prediction.
proposed
outperforms
others,
achieving
low
MAE
0.0365,
MSE
0.0074,
0.9251
on
first
dataset,
0.0469,
0.0032,
0.9868
second
demonstrating
significant
error
reduction.
Alexandria Engineering Journal,
Journal Year:
2023,
Volume and Issue:
85, P. 29 - 48
Published: Nov. 17, 2023
The
feature
selection
(FS)
problem
has
occupied
a
great
interest
of
scientists
lately
since
the
highly
dimensional
datasets
might
have
many
redundant
and
irrelevant
features.
FS
aims
to
eliminate
such
features
select
most
important
ones
that
affect
classification
performance.
Metaheuristic
algorithms
are
best
choice
solve
this
combinatorial
problem.
Recent
researchers
invented
adapted
new
algorithms,
hybridized
or
enhanced
existing
by
adding
some
operators
In
our
paper,
we
added
Coati
optimization
algorithm
(CoatiOA).
first
operator
is
adaptive
s-best
mutation
enhance
balance
between
exploration
exploitation.
second
directional
rule
opens
way
discover
search
space
thoroughly.
final
enhancement
controlling
direction
toward
global
best.
We
tested
proposed
mCoatiOA
in
solving)
solving
challenging
problems
from
CEC'20
test
suite.
performance
was
compared
with
Dandelion
Optimizer
(DO),
African
vultures
(AVOA),
Artificial
gorilla
troops
optimizer
(GTO),
whale
(WOA),
Fick's
Law
Algorithm
(FLA),
Particle
swarm
(PSO),
Harris
hawks
(HHO),
Tunicate
(TSA).
According
average
fitness,
it
can
be
observed
method,
mCoatiOA,
performs
better
than
other
on
8
functions.
It
lower
standard
deviation
values
competitive
algorithms.
Wilcoxon
showed
results
obtained
significantly
different
those
rival
been
as
algorithm.
Fifteen
benchmark
various
types
were
collected
UCI
machine-learning
repository.
Different
evaluation
criteria
used
determine
effectiveness
method.
achieved
comparison
published
methods.
mean
75%
datasets.
Computer Modeling in Engineering & Sciences,
Journal Year:
2023,
Volume and Issue:
139(3), P. 2557 - 2604
Published: Dec. 26, 2023
This
research
paper
presents
a
novel
optimization
method
called
the
Synergistic
Swarm
Optimization
Algorithm
(SSOA).The
SSOA
combines
principles
of
swarm
intelligence
and
synergistic
cooperation
to
search
for
optimal
solutions
efficiently.A
mechanism
is
employed,
where
particles
exchange
information
learn
from
each
other
improve
their
behaviors.This
enhances
exploitation
promising
regions
in
space
while
maintaining
exploration
capabilities.Furthermore,
adaptive
mechanisms,
such
as
dynamic
parameter
adjustment
diversification
strategies,
are
incorporated
balance
exploitation.By
leveraging
collaborative
nature
integrating
cooperation,
aims
achieve
superior
convergence
speed
solution
quality
performance
compared
algorithms.The
effectiveness
proposed
investigated
solving
23
benchmark
functions
various
engineering
design
problems.The
experimental
results
highlight
potential
addressing
challenging
problems,
making
it
tool
wide
range
applications
beyond.Matlab
codes
available
at:
https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic
Transactions of the Institute of Measurement and Control,
Journal Year:
2024,
Volume and Issue:
46(10), P. 1924 - 1942
Published: Jan. 18, 2024
This
paper
introduces
a
novel
metaheuristic
algorithm
named
the
opposition-based
cooperation
search
with
Nelder–Mead
(OCSANM).
enhanced
builds
upon
(CSA)
by
incorporating
learning
(OBL)
and
simplex
method.
The
primary
application
of
this
is
design
fractional-order
proportional–integral–derivative
(FOPID)
controller
for
buck
converter
system.
A
comprehensive
evaluation
conducted
using
statistical
boxplot
analysis,
nonparametric
tests
convergence
response
comparisons
to
assess
algorithm’s
performance
confirm
its
superiority
over
CSA.
Furthermore,
FOPID-controlled
system
based
on
OCSANM
compared
two
top-performing
algorithms:
one
hybridized
approach
Lévy
flight
distribution
simulated
annealing
(LFDSA)
other
employing
improved
hunger
games
(IHGS)
algorithm.
comparison
encompasses
transient
frequency
responses,
indices
robustness
analysis.
results
reveal
notable
advantages
proposed
OCSANM-based
system,
including
25.8%
8.7%
faster
rise
times,
26%
8.8%
settling
times
best-performing
approaches,
namely
LFDSA
IHGS,
respectively.
In
addition,
exhibits
34.7%
9.6%
wider
bandwidth
than
existing
approaches-based
systems.
Incorporating
voltage
current
responses
converter’s
switched
circuit
FOPID
further
underscores
effectiveness.
To
provide
assessment,
also
compares
approach’s
time
domain
those
17
state-of-the-art
approaches
attempting
control
systems
similarly.
These
findings
affirm
effectiveness
in
designing
controllers
