Mathematical Biosciences & Engineering,
Год журнала:
2023,
Номер
20(9), С. 17242 - 17271
Опубликована: Янв. 1, 2023
The
equilibrium
optimizer
(EO)
algorithm
is
a
newly
developed
physics-based
optimization
algorithm,
which
inspired
by
mixed
dynamic
mass
balance
equation
on
controlled
fixed
volume.
EO
has
number
of
strengths,
such
as
simple
structure,
easy
implementation,
few
parameters
and
its
effectiveness
been
demonstrated
numerical
problems.
However,
the
canonical
still
presents
some
drawbacks,
poor
between
exploration
exploitation
operation,
tendency
to
get
stuck
in
local
optima
low
convergence
accuracy.
To
tackle
these
limitations,
this
paper
proposes
new
EO-based
approach
with
an
adaptive
gbest-guided
search
mechanism
chaos
(called
chaos-based
(ACEO)).
Firstly,
injected
enrich
population
diversity
expand
range.
Next,
incorporated
enable
escape
from
optima.
ACEO
23
classical
benchmark
functions,
compared
EO,
variants
other
frontier
metaheuristic
approaches.
experimental
results
reveal
that
method
remarkably
outperforms
competitors.
In
addition,
implemented
solve
mobile
robot
path
planning
(MRPP)
task,
typical
techniques.
comparison
indicates
beats
competitors,
can
provide
high-quality
feasible
solutions
for
MRPP.
Alexandria Engineering Journal,
Год журнала:
2023,
Номер
87, С. 148 - 163
Опубликована: Дек. 22, 2023
Vegetation
evolution
(VEGE)
is
a
newly
proposed
meta-heuristic
algorithm
(MA)
with
excellent
exploitation
but
relatively
weak
exploration
capacity.
We
thus
focus
on
further
balancing
the
and
of
VEGE
well
to
improve
overall
optimization
performance.
This
paper
proposes
an
improved
Q-learning
based
VEGE,
we
design
archive
provide
variety
search
strategies,
each
contains
four
efficient
easy-implemented
strategies.
In
addition,
online
Q-Learning,
as
ε-greedy
scheme,
are
employed
decision-maker
role
learn
knowledge
from
past
process
determine
strategy
for
individual
automatically
intelligently.
numerical
experiments,
compare
our
QVEGE
eight
state-of-the-art
MAs
including
original
CEC2020
benchmark
functions,
twelve
engineering
problems,
wireless
sensor
networks
(WSN)
coverage
problems.
Experimental
statistical
results
confirm
that
demonstrates
significant
enhancements
stands
strong
competitor
among
existing
algorithms.
The
source
code
publicly
available
at
https://github.com/RuiZhong961230/QVEGE.
Engineering Optimization,
Год журнала:
2024,
Номер
56(11), С. 1845 - 1879
Опубликована: Янв. 4, 2024
This
article
proposes
a
solution
to
the
Dynamic
Economic
Emission
Dispatch
(DEED)
problem,
which
incorporates
wind,
solar
and
plug-in
electric
vehicles
(PEVs)
into
optimization
challenge.
The
new
model,
called
Wind-Solar-Plug
in
Electric
Vehicle
(WSPEV)
DEED,
utilizes
an
technique
Oppositional-based
Equilibrium
Optimizer
(OEO)
method
with
Weibull
Beta
distributions
model
wind
resources.
charging
discharging
patterns
of
PEVs
are
also
considered
model.
proposed
approach
is
evaluated
through
several
scenarios
involving
Renewable
Energy
Sources
(RESs)
PEVs,
simulation
results
demonstrate
effectiveness
achieving
sustainable
cost-effective
power
system.
WSPEV
DEED
provides
valuable
crucial
for
successfully
integrating
RESs
system
future.
Alexandria Engineering Journal,
Год журнала:
2023,
Номер
81, С. 469 - 488
Опубликована: Сен. 22, 2023
There
are
many
tricky
optimization
problems
in
real
life,
and
metaheuristic
algorithms
the
most
effective
way
to
solve
at
a
lower
cost.
The
dung
beetle
algorithm
(DBO)
is
more
innovative
proposed
2022,
which
affected
by
action
of
beetles
such
as
ball
rolling,
foraging,
reproduction.
Therefore,
A
based
on
quasi-oppositional
learning
Q-learning
(QOLDBO).
First,
quantum
state
update
idea
cleverly
integrated
into
increase
randomness
generated
population.
And
best
behavior
pattern
selected
adding
rolling
stage
improve
search
effect.
In
addition,
variable
spiral
local
domain
method
make
up
for
shortage
developing
only
around
neighborhood
optimum.
For
optimal
solution
each
iteration,
dimensional
adaptive
Gaussian
variation
retained.
Experimental
performance
tests
show
that
QOLDBO
performs
well
both
benchmark
test
functions
CEC
2017.
Simultaneously,
validity
verified
several
classical
practical
application
engineering
problems.
Results in Engineering,
Год журнала:
2024,
Номер
21, С. 101745 - 101745
Опубликована: Янв. 4, 2024
Online
supply
chain
management
(OSCM)
is
the
smart
way
to
deal
with
vast
amounts
of
data
that
come
in
from
customers
a
disorganized
system
meet
quantities,
volumes,
and
types
customer
packages
during
both
delivery
pick-up
phases
using
new
design
vehicle
boxes
managed
by
IoT
track
their
requests
based
on
scheduling
sorting
them
make
Hamiltonian
route
guarantees
shortest
travel
distance.
The
OSCM
framework
consists
two
sequential
phases.
1st
phase
has
four
recruitment
stages.
stage
discusses
exploration
resources
(the
relationship
between
client
vehicle)
receive
customers'
(Heijunka
growth
radius),
then
moves
maturity
build
one-way
direction.
tackling
Heijunka
matrix
fed
through
deep
learning
classify
into
many
conditional
clusters
according
request
forecasting
prediction
value,
which
stop
condition
cluster
radius
next
three
This
study
finds
XGboost
outperforms
Ada-boost
14.352
%
stage.
A
heuristic
rule
NWBS
enhances
FP-Growth
algorithm
over
ECLAT
7.648
classification
Phase
II
interested
reducing
load
unloading
activity
time.
problem
describes
needing
more
than
different
service
at
same
point
(i.e.,
chaotic
unstable
interaction
leads
delivery).
Therefore,
online
tracking
logistic
routing
Smart
Lean
supports
will
enhance
SCM,
increasing
visited
points
31.2
improving
profit
41
%.
