International Journal of Computing,
Год журнала:
2024,
Номер
unknown, С. 287 - 293
Опубликована: Июль 1, 2024
The
International
Timetabling
Competition
(ITC)
2021
focuses
on
sports
scheduling,
a
domain
intricately
connected
to
optimizing
combinatorics
problems.
Within
the
framework
of
ITC
challenge,
crucial
task
is
precisely
allocate
matches
their
designated
time
slots.
Addressing
this
challenge
involves
utilization
Adaptive
Learning
Modified
Great
Deluge
(ALMGD)
algorithm,
which
belongs
realm
hyper-heuristics.
This
algorithm
represents
an
evolutionary
step
from
foundational
great
deluge
incorporating
acceptance
mechanism
woven
with
self-adaptive
learning.
To
assess
its
efficacy,
performance
ALMGD
scrutinized
through
comparative
analysis
hill
climbing
and
algorithms.
As
result,
proposed
can
produce
solution
that
superior
comparison
algorithm.
modified
reduce
penalty
by
36%,
while
only
29%
reaches
34%.
Mathematics,
Год журнала:
2024,
Номер
12(8), С. 1249 - 1249
Опубликована: Апрель 20, 2024
Stagnation
at
local
optima
represents
a
significant
challenge
in
bio-inspired
optimization
algorithms,
often
leading
to
suboptimal
solutions.
This
paper
addresses
this
issue
by
proposing
hybrid
model
that
combines
the
Orca
predator
algorithm
with
deep
Q-learning.
The
is
an
technique
mimics
hunting
behavior
of
orcas.
It
solves
complex
problems
exploring
and
exploiting
search
spaces
efficiently.
Deep
Q-learning
reinforcement
learning
neural
networks.
integration
aims
turn
stagnation
problem
into
opportunity
for
more
focused
effective
exploitation,
enhancing
technique’s
performance
accuracy.
proposed
leverages
biomimetic
strengths
identify
promising
regions
nearby
space,
complemented
fine-tuning
capabilities
navigate
these
areas
precisely.
practical
application
approach
evaluated
using
high-dimensional
Heartbeat
Categorization
Dataset,
focusing
on
feature
selection
problem.
dataset,
comprising
electrocardiogram
signals,
provided
robust
platform
testing
our
model.
Our
experimental
results
are
encouraging,
showcasing
strategy’s
capability
relevant
features
without
significantly
compromising
metrics
machine
models.
analysis
was
performed
comparing
improved
method
against
its
native
version
set
state-of-the-art
algorithms.
Biomimetics,
Год журнала:
2024,
Номер
9(9), С. 516 - 516
Опубликована: Авг. 27, 2024
Hyper-heuristic
algorithms
are
known
for
their
flexibility
and
efficiency,
making
them
suitable
solving
engineering
optimization
problems
with
complex
constraints.
This
paper
introduces
a
self-learning
hyper-heuristic
algorithm
based
on
genetic
(GA-SLHH)
designed
to
tackle
the
logistics
scheduling
problem
of
prefabricated
modular
cabin
units
(PMCUs)
in
cruise
ships.
can
be
regarded
as
multi-objective
fuzzy
collaborative
problem.
effectively
avoid
extensive
evaluation
repair
infeasible
solutions
during
iterative
process,
which
is
common
issue
meta-heuristic
algorithms.
The
GA-SLHH
employs
combined
strategy
its
high-level
(HLS),
optimizing
low-level
heuristics
(LLHs)
while
uncovering
potential
relationships
between
adjacent
decision-making
stages.
LLHs
utilize
classic
rules
solution
support.
Multiple
sets
numerical
experiments
demonstrate
that
exhibits
stronger
comprehensive
ability
stability
when
this
Finally,
validity
addressing
real-world
issues
ship
manufacturing
companies
validated
through
practical
enterprise
cases.
results
case
show
scheme
solved
using
proposed
reduce
transportation
time
by
up
37%.
