Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 30, 2024
Abstract
This
study
proposes
a
novel
physics-inspired
metaheuristic
algorithm
named
Forest
Fire
Ash
Optimizer
(FFA).
Inspired
by
the
characteristics
of
ash
movement
in
different
forest
fire
burning
stages,
intelligently
transformed
these
regular
behaviors
into
variety
unique
algorithmic
mechanisms,
including
four-population
mechanism
based
on
fitness
and
denseness
partitioning,
an
inverse
proportionality
selection
for
elite
influence,
strategy
grounded
actual
physical
phenomena.
These
components
complement
each
other
to
enable
flexible
regulation
exploration
exploitation,
i.e.,
two
phases
are
not
merely
sequential,
but
change
dynamically
depending
search
status
FFA
agents.
To
verify
effectiveness
proposed
algorithm,
is
qualitatively
analyzed
using
CEC-2022
test
suite.
Additionally,
classical
standard
suite,
CEC-2017
suite
conducted
compare
performance
with
9
advanced
algorithms.
The
results
demonstrate
that
excels
performance,
exhibiting
high
stability,
flexibility,
robustness.
Finally,
applied
challenging
real-world
engineering
optimization
problems.
indicate
that,
compared
competing
algorithms,
provides
superior
more
solutions,
predicting
its
potential
in-depth
applications
fields.
PLoS ONE,
Год журнала:
2024,
Номер
19(8), С. e0308474 - e0308474
Опубликована: Авг. 19, 2024
This
research
article
presents
the
Multi-Objective
Hippopotamus
Optimizer
(MOHO),
a
unique
approach
that
excels
in
tackling
complex
structural
optimization
problems.
The
(HO)
is
novel
meta-heuristic
methodology
draws
inspiration
from
natural
behaviour
of
hippos.
HO
built
upon
trinary-phase
model
incorporates
mathematical
representations
crucial
aspects
Hippo's
behaviour,
including
their
movements
aquatic
environments,
defense
mechanisms
against
predators,
and
avoidance
strategies.
conceptual
framework
forms
basis
for
developing
multi-objective
(MO)
variant
MOHO,
which
was
applied
to
optimize
five
well-known
truss
structures.
Balancing
safety
precautions
size
constraints
concerning
stresses
on
individual
sections
constituent
parts,
these
problems
also
involved
competing
objectives,
such
as
reducing
weight
structure
maximum
nodal
displacement.
findings
six
popular
methods
were
used
compare
results.
Four
industry-standard
performance
measures
this
comparison
qualitative
examination
finest
Pareto-front
plots
generated
by
each
algorithm.
average
values
obtained
Friedman
rank
test
analysis
unequivocally
showed
MOHO
outperformed
other
resolving
significant
quickly.
In
addition
finding
preserving
more
Pareto-optimal
sets,
recommended
algorithm
produced
excellent
convergence
variance
objective
decision
fields.
demonstrated
its
potential
navigating
objectives
through
diversity
analysis.
Additionally,
swarm
effectively
visualize
MOHO's
solution
distribution
across
iterations,
highlighting
superior
behaviour.
Consequently,
exhibits
promise
valuable
method
issues.
International journal of intelligent engineering and systems,
Год журнала:
2024,
Номер
17(3), С. 816 - 828
Опубликована: Май 3, 2024
In
this
article,
a
new
human-based
metaheuristic
algorithm
named
Dollmaker
Optimization
Algorithm
(DOA)
is
introduced,
which
imitates
the
strategy
and
skill
of
dollmaker
when
making
dolls.The
basic
inspiration
DOA
derived
from
two
natural
behaviors
in
doll
process
(i)
general
changes
to
dollmaking
materials
(ii)
precise
small
on
appearance
characteristics
theory
proposed
then
modeled
mathematically
phases
exploration
based
simulation
large
made
doll-making
exploitation
performance
optimization
evaluated
twenty-three
standard
benchmark
functions
unimodal,
high-dimensional
multimodal,
fixed-dimensional
multimodal
types.The
results
show
that
has
achieved
suitable
for
problems
with
its
ability
exploration,
exploitation,
balance
them
during
search
process.Comparison
twelve
competing
algorithms
shows
superior
compared
by
providing
better
all
getting
rank
first
best
optimizer.In
addition,
efficiency
handling
real-world
applications
four
engineering
design
problems.Simulation
acceptable
real
world
values
variables
objective
algorithms.
Applied Sciences,
Год журнала:
2025,
Номер
15(2), С. 603 - 603
Опубликована: Янв. 9, 2025
The
Material
Generation
Optimization
(MGO)
algorithm
is
an
innovative
approach
inspired
by
material
chemistry
which
emulates
the
processes
of
chemical
compound
formation
and
stabilization
to
thoroughly
explore
refine
parameter
space.
By
simulating
bonding
processes—such
as
ionic
covalent
bonds—MGO
generates
new
solution
candidates
evaluates
their
stability,
guiding
toward
convergence
on
optimal
values.
To
improve
its
search
efficiency,
this
paper
introduces
Enhanced
(IMGO)
algorithm,
integrates
a
Quadratic
Interpolated
Learner
Process
(QILP).
Unlike
conventional
random
selection,
QILP
strategically
selects
three
distinct
compounds,
resulting
in
increased
diversity,
more
thorough
exploration
space,
improved
resistance
local
optima.
adaptable
non-linear
adjustments
QILP’s
quadratic
function
allow
traverse
complex
landscapes
effectively.
This
IMGO,
along
with
original
MGO,
developed
support
applications
across
phases,
showcasing
versatility
enhanced
optimization
capabilities.
Initially,
both
MGO
algorithms
are
evaluated
using
several
mathematical
benchmarks
from
CEC
2017
test
suite
measure
Following
this,
applied
following
well-known
engineering
problems:
welded
beam
design,
rolling
element
bearing
pressure
vessel
design.
simulation
results
then
compared
various
established
bio-inspired
algorithms,
including
Artificial
Ecosystem
(AEO),
Fitness–Distance-Balance
AEO
(FAEO),
Chef-Based
Algorithm
(CBOA),
Beluga
Whale
(BWOA),
Arithmetic-Trigonometric
(ATOA),
Atomic
Orbital
Searching
(AOSA).
Moreover,
IMGO
tested
real
Egyptian
power
distribution
system
optimize
placement
PV
capacitor
units
aim
minimizing
energy
losses.
Lastly,
parameters
estimation
problem
successfully
solved
via
considering
commercial
RTC
France
cell.
Comparative
studies
demonstrate
that
not
only
achieves
significant
loss
reduction
but
also
contributes
environmental
sustainability
reducing
emissions,
overall
effectiveness
practical
applications.
outcomes
23
benchmark
models
average
accuracy
enhancement
65.22%
consistency
69.57%
method.
Also,
application
achieved
computational
errors
27.8%
while
maintaining
superior
stability
alternative
methods.
Expert Systems with Applications,
Год журнала:
2024,
Номер
255, С. 124777 - 124777
Опубликована: Июль 14, 2024
Accurately
estimating
the
unknown
parameters
of
photovoltaic
(PV)
models
based
on
measured
voltage-current
data
is
a
challenging
optimization
problem
due
to
its
high
nonlinearity
and
multimodality.
An
accurate
solution
this
essential
for
efficiently
simulating,
controlling,
evaluating
PV
systems.
There
are
three
different
models,
including
single-diode
model,
double-diode
triple-diode
with
five,
seven,
nine
parameters,
respectively,
proposed
represent
electrical
characteristics
systems
varying
levels
complexity
accuracy.
In
literature,
several
deterministic
metaheuristic
algorithms
have
been
used
accurately
solve
hard
problem.
However,
problem,
methods
could
not
achieve
solutions.
On
other
side,
algorithms,
also
known
as
gradient-free
methods,
somewhat
good
solutions
but
they
still
need
further
improvements
strengthen
their
performance
against
stuck-in
local
optima
slow
convergence
speed
problems.
Over
last
two
years,
recent
better
improve
avoid
tackle
continuous
majority
those
has
investigated.
Therefore,
in
paper,
nineteen
recently
published
such
Mantis
search
algorithm
(MSA),
spider
wasp
optimizer
(SWO),
light
spectrum
(LSO),
growth
(GO),
walrus
(WAOA),
hippopotamus
(HOA),
black-winged
kite
(BKA),
quadratic
interpolation
(QIO),
sinh
cosh
(SCHA),
exponential
distribution
(EDO),
optical
microscope
(OMA),
secretary
bird
(SBOA),
Parrot
Optimizer
(PO),
Newton-Raphson-based
(NRBO),
crested
porcupine
(CPO),
differentiated
creative
(DCS),
propagation
(PSA),
one-to-one
(OOBO),
triangulation
topology
aggregation
(TTAO),
studied
clarify
effectiveness
models.
addition,
collaborate
functions,
namely
Lambert
W-Function
Newton-Raphson
Method,
aid
solving
I-V
curve
equations
more
accurately,
thereby
improving
Those
assessed
using
four
well-known
solar
cells
modules
compared
each
metrics,
best
fitness,
average
worst
standard
deviation
(SD),
Friedman
mean
rank,
speed;
multiple-comparison
test
compare
difference
between
ranks.
Results
comparison
show
that
SWO
efficient
effective
SDM,
DDM,
TDM
over
modules,
Method
equations.
study
reports
perform
poorly
when
applied
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Фев. 4, 2025
Recently,
chaotic
maps
(CMs)
have
been
employed
in
many
optimization
algorithms
as
a
motivator
to
find
better
solution
non-convex
engineering
problems
since
they
can
avoid
local
optima
and
the
near-optimal
rapidly.
In
this
article,
metaheuristic,
physics-based
algorithm
called
transient
search
(CTSO)
is
developed
solve
23
benchmark
functions,
including
uni-
multi-modal
functions.
Nine
CMs
integrated
into
TSO
improve
its
capabilities
by
applying
various
scenarios
for
improving
random
numbers.
Further,
proposed
CTSO
was
compared
with
original
using
Wilcoxon
p-value
test,
non-parametric
sign
t-test,
convergence
curves,
elapsed
time.
Furthermore,
has
solving
real-life
design
problems,
coil
spring,
welded
beam,
pressure
vessel
design,
where
performed
than
some
recent
finding
best
design.
