Mathematics,
Год журнала:
2024,
Номер
12(23), С. 3726 - 3726
Опубликована: Ноя. 27, 2024
In
this
paper,
an
improved
hybrid
genetic-hierarchical
algorithm
for
the
solution
of
quadratic
assignment
problem
(QAP)
is
presented.
The
based
on
genetic
search
combined
with
hierarchical
(hierarchicity-based
multi-level)
iterated
tabu
procedure.
following
are
two
main
scientific
contributions
paper:
(i)
enhanced
two-level
primary
(master)-secondary
(slave)
proposed;
(ii)
augmented
universalized
multi-strategy
perturbation
(mutation
process)—which
integrated
within
a
multi-level
algorithm—is
implemented.
proposed
scheme
enables
efficient
balance
between
intensification
and
diversification
in
process.
computational
experiments
have
been
conducted
using
QAP
instances
sizes
up
to
729.
results
from
demonstrate
outstanding
performance
new
approach.
This
especially
obvious
small-
medium-sized
instances.
Nearly
90%
runs
resulted
(pseudo-)optimal
solutions.
Three
best-known
solutions
achieved
very
hard,
challenging
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Янв. 29, 2024
Abstract
This
study
investigates
the
no-wait
flow
shop
scheduling
problem
and
proposes
a
hybrid
(HES-IG)
algorithm
that
utilizes
makespan
as
objective
function.
To
address
complexity
of
this
NP-hard
problem,
HES-IG
combines
evolution
strategies
(ES)
iterated
greedy
(IG)
algorithm,
hybridizing
algorithms
helps
different
mitigate
their
weaknesses
leverage
respective
strengths.
The
ES
begins
with
random
initial
solution
uses
an
insertion
mutation
to
optimize
solution.
Reproduction
is
carried
out
using
(1
+
5)-ES,
generating
five
offspring
from
one
parent
randomly.
selection
process
employs
(µ
λ)-ES,
allowing
excellent
solutions
survive
multiple
generations
until
better
surpasses
them.
IG
algorithm’s
straightforward
search
mechanism
aids
in
further
improving
avoiding
local
minima.
destruction
operator
randomly
removes
d-jobs,
which
are
then
inserted
by
construction
operator.
single
approach,
while
acceptance–rejection
criteria
based
on
constant
temperature.
Parameters
both
calibrated
Multifactor
analysis
variance
technique.
performance
other
Wilcoxon
signed
test.
tested
21
Nos.
Reeves
30
Taillard
benchmark
problems.
has
found
15
lower
bound
values
for
Similarly,
Computational
results
indicate
outperforms
available
techniques
literature
all
sizes.
Engineering Optimization,
Год журнала:
2024,
Номер
unknown, С. 1 - 23
Опубликована: Апрель 12, 2024
With
the
complexity
involved
in
manufacturing
products,
many
companies
use
multiple
processes
to
complete
product
processing.
Most
studies
have
been
concerned
with
single
production
but
neglected
widespread
joint
flowshop
scheduling
problem
(JFSP).
In
this
article,
a
cooperative
grey
wolf
optimizer
(CGWO)
is
developed
solve
JFSP.
First,
according
features
of
JFSP,
corresponding
mathematical
model
constructed,
and
three
collaborative
strategies
random
generation
are
proposed
initialize
population.
process
searching
for
prey,
discretized
search
prey
update
mechanism
proposed,
which
conducive
balancing
exploration
exploitation.
An
energy-saving
strategy
decrease
energy
consumption.
Moreover,
four
local
mechanisms
different
optimization
objectives
enhance
performance
method
attacking
prey.
The
results
show
that
CGWO
effective
solving
IEEE Access,
Год журнала:
2024,
Номер
12, С. 106333 - 106358
Опубликована: Янв. 1, 2024
This
study
proposes
a
capacitated
vehicle
routing
problem
(CVRP)
approach
to
optimise
Vehicle
Routing
Problem
(VRP)
and
pesticides
spraying.
The
VRP
consists
of
finding
the
route
which
covers
every
point
certain
area
interest.
paper
considers
search
spraying
mission,
using
group
Unmanned
Aerial
Vehicles
(UAVs).
In
this
scenario,
objective
is
minimise
total
battery
consumption
level
tank
not
exceed
their
maximum
capacities.
A
hybrid
metaheuristic
optimisation
algorithm
formulated
by
integrating
Genetic
Algorithm
(GA)
with
guided
local
called
genetic
(GGA).
performance
proposed
GGA
compared
four
single-solution
based
algorithms
(Guided
Local
Search
[GLS],
Tabu
[TS],
Simulated
Annealing
[SA],
Iterated
[ILS])
two
population-based
metaheuristics
(GA
Particle
Swarm
Optimisation
[PSO]
algorithm).
results
revealed
that
outperformed
other
in
most
instances.
showed
competitive
results,
closely
following
TS's
across
different
scenarios.
evaluation
conducted
analysing
its
mean,
standard
deviation,
best
solution,
worst
solution
ten
iterations.
addition,
Wilcoxon
signed-rank
test
36
discussion
provide
confirmation
method
beat
algorithms.
