Drones,
Год журнала:
2023,
Номер
7(9), С. 544 - 544
Опубликована: Авг. 22, 2023
Unmanned
aerial
vehicles
(UAVs)
are
extensively
employed
for
urban
image
captures
and
the
reconstruction
of
large-scale
3D
models
due
to
their
affordability
versatility.
However,
most
commercial
flight
software
lack
support
adaptive
capture
multi-view
images.
Furthermore,
limited
performance
battery
capacity
a
single
UAV
hinder
efficient
capturing
scenes.
To
address
these
challenges,
this
paper
presents
novel
method
multi-UAV
continuous
trajectory
planning
aimed
at
reconstructions
scene.
Our
primary
contribution
lies
in
development
path
framework
rooted
task
search
principles.
Within
framework,
we
initially
ascertain
optimal
locations
images
by
assessing
scene
reconstructability,
thereby
enhancing
overall
quality
reconstructions.
curtail
energy
costs
trajectories
allocating
sequences,
characterized
minimal
corners
lengths,
among
multiple
UAVs.
Ultimately,
integrate
considerations
costs,
safety,
reconstructability
into
unified
optimization
process,
facilitating
paths
Empirical
evaluations
demonstrate
efficacy
our
approach
collaborative
full-scene
UAVs,
achieving
low
while
attaining
high-quality
IEEE Transactions on Cybernetics,
Год журнала:
2022,
Номер
53(8), С. 5276 - 5289
Опубликована: Авг. 22, 2022
Feature
selection
(FS)
has
received
significant
attention
since
the
use
of
a
well-selected
subset
features
may
achieve
better
classification
performance
than
that
full
in
many
real-world
applications.
It
can
be
considered
as
multiobjective
optimization
consisting
two
objectives:
1)
minimizing
number
selected
and
2)
maximizing
performance.
Ant
colony
(ACO)
shown
its
effectiveness
FS
due
to
problem-guided
search
operator
flexible
graph
representation.
However,
there
lacks
an
effective
ACO-based
approach
for
handle
problematic
characteristics
originated
from
feature
interactions
highly
discontinuous
Pareto
fronts.
This
article
presents
Information-theory-based
Nondominated
Sorting
ACO
(called
INSA)
solve
aforementioned
difficulties.
First,
probabilistic
function
is
modified
based
on
information
theory
identify
importance
features;
second,
new
strategy
designed
construct
solutions;
third,
novel
pheromone
updating
devised
ensure
high
diversity
tradeoff
solutions.
INSA's
compared
with
four
machine-learning-based
methods,
representative
single-objective
evolutionary
algorithms,
six
state-of-the-art
ones
13
benchmark
datasets,
which
consist
both
low
high-dimensional
samples.
The
empirical
results
verify
INSA
able
obtain
solutions
using
whose
count
similar
or
less
those
obtained
by
peers.
In
contrast
to
rotorcraft,
fixed-wing
unmanned
aerial
vehicles
(UAVs)
encounter
a
unique
challenge
in
path
planning
due
the
necessity
of
accounting
for
turning
radius
constraint.
This
research
focuses
on
coverage
planning,
aiming
determine
optimal
trajectories
UAVs
thoroughly
explore
designated
areas
interest.
To
address
this
challenge,
Linear
Programming—Fuzzy
C-Means
with
Pigeon-Inspired
Optimization
algorithm
(LP-FCMPIO)
is
proposed.
Initially
considering
constraint,
linear-programming-based
model
UAV
established.
Subsequently,
partition
multiple
effectively,
an
improved
fuzzy
clustering
introduced.
Employing
pigeon-inspired
optimization
as
final
step,
approximately
solution
sought.
Simulation
experiments
demonstrate
that
LP-FCMPIO,
when
compared
traditional
FCM,
achieves
more
balanced
effect.
Additionally,
PIO,
planned
flight
paths
display
task
areas,
27.5%
reduction
number
large
maneuvers.
The
experimental
results
provide
validation
effectiveness
proposed
algorithm.
IEEE Transactions on Intelligent Transportation Systems,
Год журнала:
2022,
Номер
24(8), С. 8667 - 8676
Опубликована: Авг. 25, 2022
Suffering
from
environmental
distress
like
carbon
emissions,
traffic
restrictions
have
been
enforced
extensively
in
distribution
logistics.
Reasonable
arrangement
of
urban
freight
transportation
can
effectively
improve
efficiency,
reduce
costs,
and
alleviate
the
impact
In
response
to
increasingly
stringent
restrictions,
we
establish
a
multi-objective
optimization
model,
including
minimum
distributions
emissions.
Given
that
limits
battery
capacity
cargo
capacity,
build
green
vehicle
routing
problem
with
soft
time
windows
(GVRPTW)
model
heterogenous
fleets.
this
study,
three
different
factors,
is
restricted
area,
travel
vehicles,
tax
prices,
are
discussed
details.
order
solve
NP-hard
propose
an
improved
ant
colony
algorithm
(IACO)
by
optimizing
state
transition
probability,
verifies
worth
algorithm.
The
experimental
results
explore
impacts
restriction
policies
on
formulation
scheme
offer
reference
opinions
for
government
formulate
reasonable
better
guide
logistics
enterprises
