Fractal and Fractional,
Journal Year:
2024,
Volume and Issue:
8(11), P. 625 - 625
Published: Oct. 24, 2024
Ocean
buoys
play
a
critical
role
in
marine
hydrological,
water
quality,
and
meteorological
monitoring,
with
applications
navigation,
environmental
observation,
communication.
However,
accurately
modeling
deploying
multi-buoy
system
the
complex
environment
presents
significant
challenges.
To
address
these
challenges,
this
study
proposes
an
enhanced
deployment
strategy
using
tuna
swarm
optimizer
fractional-order
calculus
method
for
observation.
The
proposed
first
introduces
detailed
observation
model
that
precisely
captures
performance
of
terms
coverage
communication
efficiency.
By
integrating
ratio
energy
consumption,
we
establish
optimal
model.
leverages
tent
chaotic
mapping
to
improve
diversity
initial
solution
generation
incorporates
strengthen
its
search
capabilities.
Simulation
experiments
statistical
analysis
verify
effectiveness
model,
achieving
best
system,
reaching
final
fitness
value
0.190052
at
iteration
449,
outperforming
TSA,
PSO,
GWO,
WOA.
These
results
highlight
potential
optimizing
Symmetry,
Journal Year:
2025,
Volume and Issue:
17(2), P. 276 - 276
Published: Feb. 11, 2025
In
this
paper,
a
Distributed
Mixed
No-Idle
Permutation
Flowshop
Scheduling
Problem
with
Sequence-Dependent
Setup
Times
(DMNIPFSP/SDST)
is
studied.
Firstly,
multi-objective
optimization
model
completion
time
(makespan),
Total
Energy
Consumption
(TEC),
and
Tardiness
(TT)
as
objectives
established.
Based
on
problem
characteristics
characteristics,
Q-Learning
Evolutionary
Algorithm
(QLEA)
proposed.
Secondly,
in
order
to
improve
the
quality
diversity
of
initial
solution,
two
improved
initialization
strategies
are
solved
(In
distributed
system,
symmetry
design
adopted
ensure
that
load
each
workstation
relatively
balanced
different
periods,
avoid
resource
waste
or
bottleneck,
achieve
goal
no
idle.),
novel
population
updating
mechanism
designed
balance
ability
global
exploration
local
development
algorithm.
At
same
time,
variable
neighborhood
search
based
used
refine
non-dominated
thus
guiding
evolution.
Finally,
simulation
results
show
method
has
good
performance
solving
DMNIPFSP/SDST
can
provide
economic
benefits
for
enterprises.
Biomimetics,
Journal Year:
2024,
Volume and Issue:
9(7), P. 388 - 388
Published: June 26, 2024
The
Sine-Levy
tuna
swarm
optimization
(SLTSO)
algorithm
is
a
novel
method
based
on
the
sine
strategy
and
Levy
flight
guidance.
It
presented
as
solution
to
shortcomings
of
(TSO)
algorithm,
which
include
its
tendency
reach
local
optima
limited
capacity
search
worldwide.
This
updates
locations
using
technique
greedy
approach
generates
initial
solutions
an
elite
reverse
learning
process.
Additionally,
it
offers
individual
location
called
golden
sine,
enhances
algorithm's
explore
widely
steer
clear
optima.
To
plan
UAV
paths
safely
effectively
in
complex
obstacle
environments,
SLTSO
considers
constraints
such
geographic
airspace
obstacles,
along
with
performance
metrics
like
environment,
space,
distance,
angle,
altitude,
threat
levels.
effectiveness
verified
by
simulation
creation
path
planning
model.
Experimental
results
show
that
displays
faster
convergence
rates,
better
precision,
shorter
smoother
paths,
concomitant
reduction
energy
usage.
A
drone
can
now
map
route
far
more
thanks
these
improvements.
Consequently,
proposed
demonstrates
both
efficacy
superiority
applications.
Electronics,
Journal Year:
2025,
Volume and Issue:
14(8), P. 1663 - 1663
Published: April 19, 2025
This
article
aims
to
review
the
industrial
applications
of
AI-based
intelligent
system
algorithms
in
manufacturing
sector
find
latest
methods
used
for
sustainability
and
optimisation.
In
contrast
previous
articles
that
broadly
summarised
existing
methods,
this
paper
specifically
emphasises
most
recent
techniques,
providing
a
systematic
structured
evaluation
their
practical
within
sector.
The
primary
objective
study
is
algorithms,
including
metaheuristics,
evolutionary
learning-based
sector,
particularly
through
lens
optimisation
workflow
production
lines,
Job
Shop
Scheduling
Problems
(JSSPs).
It
critically
evaluates
various
solving
JSSPs,
with
particular
focus
on
Flexible
(FJSPs),
more
advanced
form
JSSPs.
process
consists
several
intricate
operations
must
be
meticulously
planned
scheduled
executed
effectively.
regard,
Production
scheduling
best
possible
schedule
maximise
one
or
performance
parameters.
An
integral
part
JSSP
both
traditional
smart
manufacturing;
however,
research
focuses
concept
general,
which
pertains
concerns
aim
maximising
operational
efficiency
by
reducing
time
costs.
A
common
feature
among
studies
lack
consistent
effective
solution
minimise
energy
consumption,
thus
accelerating
minimal
resources.
