Journal of Intelligent Systems,
Год журнала:
2024,
Номер
33(1)
Опубликована: Янв. 1, 2024
Abstract
In
recent
years,
the
field
of
data
analytics
has
witnessed
a
surge
in
innovative
techniques
to
handle
ever-increasing
volume
and
complexity
data.
Among
these,
nature-inspired
algorithms
have
gained
significant
attention
due
their
ability
efficiently
mimic
natural
processes
solve
intricate
problems.
One
such
algorithm,
symbiotic
organisms
search
(SOS)
Algorithm,
emerged
as
promising
approach
for
clustering
predictive
tasks,
drawing
inspiration
from
relationships
observed
biological
ecosystems.
Metaheuristics
SOS
been
frequently
employed
discover
suitable
solutions
complicated
issues.
Despite
numerous
research
works
on
SOS-based
techniques,
there
minimal
secondary
investigations
field.
The
aim
this
study
is
fill
gap
by
performing
systematic
literature
review
(SLR)
models
focusing
various
aspects,
including
adopted
approach,
feature
selection
hybridized
combining
K-means
algorithm
with
different
algorithms.
This
aims
guide
researchers
better
understand
issues
challenges
area.
assesses
unique
articles
published
journals
conferences
over
last
ten
years
(2014–2023).
After
abstract
full-text
eligibility
analysis,
limited
number
were
considered
SLR.
findings
show
that
methods
adapted
which
CSOS,
discrete
SOS,
multiagent
are
mostly
used
applications,
binary
S-shaped
transfer
functions,
BSOSVT
also
revealed
that,
all
selected
studies
review,
only
few
specifically
focused
hybridizing
automatic
application.
Finally,
analyzes
gaps
prospects
methods.
Ain Shams Engineering Journal,
Год журнала:
2024,
Номер
15(6), С. 102719 - 102719
Опубликована: Март 2, 2024
This
study
proposes
a
two-step
method
for
generating
switching
angles
in
renewable
energy
systems
that
use
multi-level
inverters
(MLIs)
to
reduce
low-order
harmonics.
The
Selective
Harmonic
Elimination
Pulse
Width
Modulation
(SHE-PWM)
technique
is
used
MLI
control,
but
it
can
be
computationally
intensive
real-time
applications.
To
address
this
challenge,
the
proposed
approach
consists
of
two-stage
process.
In
first
stage,
SHE
equations
are
solved
offline
using
artificial
ecosystem-based
optimization
(AEO).
obtained
then
train
an
neural
network
(ANN)
prediction
model
second
stage.
AEO-ANN-based
SHE-PWM
applied
reduced-switching,
3-phase,
7-level
MLI.
Simulations
MATLAB/SIMULINK
show
achieves
accurate
voltage
control
with
less
than
0.2%
error,
even
changing
voltages,
and
reduces
selected
harmonics
0.05%.
desired
output
exhibits
minimal
total
harmonic
distortion
(THD).
offers
promising
way
generate
MLIs,
improving
power
quality
reducing
distortion.
Decision Analytics Journal,
Год журнала:
2023,
Номер
10, С. 100371 - 100371
Опубликована: Ноя. 24, 2023
This
study
presents
a
comprehensive
on
the
potential
and
efficacy
of
decomposition-based
multi-objective
symbiotic
organism
search
(MOSOS/D)
algorithm
for
truss
structure
optimization.
The
investigation
is
carried
out
five
benchmark
structures,
e.g.,
37-bar,
60-bar,
72-bar,
120-bar,
200-bar
problems.
optimization
performance
MOSOS/D
compared
with
other
established
algorithms,
such
as
Multi-Objective
Evolutionary
Algorithm
Based
Decomposition,
Non-Dominated
Sorting
Genetic
Algorithm-II,
Equilibrium
Optimizer,
Marine
Predator
Algorithm,
Decomposition-Based
Heat
Transfer
Search,
Passing
Vehicle
Decomposition
Differential
Evaluation,
Symbiotic
Organisms
Multi-Verse
Optimizer
considering
several
metrics,
including
Generational
Distance,
Spacing,
Spread,
Inverted
Hypervolume
Runtime.
findings
demonstrate
MOSOS/D's
competitiveness
in
providing
non-dominated
solutions
less
space
between
them.
It
also
achieves
good
balance
convergence
coverage
and,
thus,
delivers
diverse
computational
complexity.
However,
efficiency
varies
depending
complexity
considered
structure,
some
algorithms
occasionally
outperforming
it
one
or
two
aspects
specific
benchmarks.
provides
valuable
insights
to
engineers
designers
aiming
achieve
optimal
design.
Journal of Intelligent Systems,
Год журнала:
2024,
Номер
33(1)
Опубликована: Янв. 1, 2024
Abstract
In
recent
years,
the
field
of
data
analytics
has
witnessed
a
surge
in
innovative
techniques
to
handle
ever-increasing
volume
and
complexity
data.
Among
these,
nature-inspired
algorithms
have
gained
significant
attention
due
their
ability
efficiently
mimic
natural
processes
solve
intricate
problems.
One
such
algorithm,
symbiotic
organisms
search
(SOS)
Algorithm,
emerged
as
promising
approach
for
clustering
predictive
tasks,
drawing
inspiration
from
relationships
observed
biological
ecosystems.
Metaheuristics
SOS
been
frequently
employed
discover
suitable
solutions
complicated
issues.
Despite
numerous
research
works
on
SOS-based
techniques,
there
minimal
secondary
investigations
field.
The
aim
this
study
is
fill
gap
by
performing
systematic
literature
review
(SLR)
models
focusing
various
aspects,
including
adopted
approach,
feature
selection
hybridized
combining
K-means
algorithm
with
different
algorithms.
This
aims
guide
researchers
better
understand
issues
challenges
area.
assesses
unique
articles
published
journals
conferences
over
last
ten
years
(2014–2023).
After
abstract
full-text
eligibility
analysis,
limited
number
were
considered
SLR.
findings
show
that
methods
adapted
which
CSOS,
discrete
SOS,
multiagent
are
mostly
used
applications,
binary
S-shaped
transfer
functions,
BSOSVT
also
revealed
that,
all
selected
studies
review,
only
few
specifically
focused
hybridizing
automatic
application.
Finally,
analyzes
gaps
prospects
methods.
