Energies,
Journal Year:
2024,
Volume and Issue:
17(16), P. 4104 - 4104
Published: Aug. 18, 2024
The
paper
presents
the
application
of
a
new
bio-inspired
metaheuristic
optimization
algorithm.
popularity
and
usability
different
swarm-based
algorithms
are
undeniable.
majority
known
mimic
hunting
behavior
animals.
However,
current
approach
does
not
satisfy
full
bio-diversity
inspiration
among
organisms.
Thus,
Birch-inspired
Optimization
Algorithm
(BiOA)
is
proposed
as
powerful
efficient
tool
based
on
pioneering
one
most
common
tree
species.
Birch
trees
for
their
superiority
over
other
species
in
overgrowing
spreading
across
unrestricted
terrains.
two-step
algorithm
reproduces
both
seed
transport
plant
development.
A
detailed
description
mathematical
model
given.
discussion
examination
influence
parameters
efficiency
also
provided
detail.
In
order
to
demonstrate
effectiveness
algorithm,
its
selecting
control
structure
drive
system
with
an
elastic
connection
shown.
PI
controller
two
additional
feedbacks
torque
speed
difference
between
motor
working
machine
was
selected.
rated
variable
considered.
theoretical
considerations
simulation
study
were
verified
laboratory
stand.
This
paper
introduces
a
novel
multi-strategy
enhanced
dung
beetle
optimization
(MSDBO)
algorithm
that
is
designed
to
address
several
issues
identified
in
the
standard
algorithm.
Specifically,
MSDBO
aims
enhance
convergence
speed,
reduce
susceptibility
local
optima,
and
increase
search
accuracy.
By
incorporating
three
strategies:
tent
chaotic
mapping
for
population
initialization,
golden
sinusoidal
strategy
position
updating,
Lévy
flight
balancing
exploration
exploitation,
enhanced.
The
evaluated
using
twelve
benchmark
test
functions
compared
against
five
state-of-the-art
algorithms.
results
consistently
show
exhibits
faster
speeds
more
accurate
solutions
than
other
algorithms
across
most
of
functions.
In
addition,
also
applied
optimize
parameters
valve
plate,
including
close
angle,
cross
triangle
groove
sizes,
wrap
angle.
outcomes
reveal
effectively
minimizes
pressure
ripples
piston
chamber,
resulting
reduced
flow
rate
fluctuations
noise
emission
initial
design.
study
highlights
potential
tackling
complex
nonlinear
engineering
problems.
Bioinformatics,
Journal Year:
2023,
Volume and Issue:
39(7)
Published: June 24, 2023
Mathematical
models
in
systems
biology
help
generate
hypotheses,
guide
experimental
design,
and
infer
the
dynamics
of
gene
regulatory
networks.
These
are
characterized
by
phenomenological
or
mechanistic
parameters,
which
typically
hard
to
measure.
Therefore,
efficient
parameter
estimation
is
central
model
development.
Global
optimization
techniques,
such
as
evolutionary
algorithms
(EAs),
applied
estimate
parameters
inverse
modeling,
i.e.
calibrating
minimizing
a
function
that
evaluates
measure
error
between
predictions
data.
EAs
"fittest
individuals"
generating
large
population
individuals
using
strategies
like
recombination
mutation
over
multiple
"generations."
Typically,
only
few
from
each
generation
used
create
new
next
generation.
Improved
Evolutionary
Strategy
Stochastic
Ranking
(ISRES),
proposed
Runnarson
Yao,
one
EA
widely
parameters.
ISRES
uses
information
at
most
pair
any
minimize
error.
In
this
article,
we
propose
an
strategy,
ISRES+,
builds
on
combining
all
across
generations
develop
better
understanding
fitness
landscape.
