CAAI Transactions on Intelligence Technology,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 24, 2024
Abstract
The
Runge–Kutta
optimiser
(RUN)
algorithm,
renowned
for
its
powerful
optimisation
capabilities,
faces
challenges
in
dealing
with
increasing
complexity
real‐world
problems.
Specifically,
it
shows
deficiencies
terms
of
limited
local
exploration
capabilities
and
less
precise
solutions.
Therefore,
this
research
aims
to
integrate
the
topological
search
(TS)
mechanism
gradient
rule
(GSR)
into
framework
RUN,
introducing
an
enhanced
algorithm
called
TGRUN
improve
performance
original
algorithm.
TS
employs
a
circular
scheme
conduct
thorough
solution
regions
surrounding
each
solution,
enabling
careful
examination
valuable
areas
enhancing
algorithm’s
effectiveness
exploration.
To
prevent
from
becoming
trapped
optima,
GSR
also
integrates
descent
principles
direct
wider
investigation
global
space.
This
study
conducted
serious
experiments
on
IEEE
CEC2017
comprehensive
benchmark
function
assess
TGRUN.
Additionally,
evaluation
includes
engineering
design
feature
selection
problems
serving
as
additional
test
assessing
validation
outcomes
indicate
significant
improvement
accuracy
International Journal of Computational Intelligence Systems,
Год журнала:
2024,
Номер
17(1)
Опубликована: Июль 3, 2024
Abstract
The
multi-quay
berth
allocation
problem
(MQBAP)
is
an
important
in
the
planning
of
seaside
operations
(POSO)
to
find
best
berthing
solution
for
all
vessels.
In
this
paper,
efficient
method
based
on
equilibrium
optimizer
(EO)
proposed
MQBAP.
dynamic
multi-swarm
strategy
(DMS)
improve
rapid
decline
population
diversity
during
iterative
process
EO,
which
subsequently
applied
a
certain
improvement
also
made
original
model
MQBAP
by
proposing
alternate
quay
selection
mechanism,
aims
make
more
complete.
To
verify
effectiveness
algorithm
MQBAP,
paper
uses
six
test
cases
and
seven
comparative
algorithms
it
comprehensively
from
total
service
cost,
time,
location.
results
show
that
DEO
achieved
smallest
costs
7584
19,889
medium-scale,
44,998,
38,899,
57,626
large-scale
systems.
CAAI Transactions on Intelligence Technology,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 24, 2024
Abstract
The
Runge–Kutta
optimiser
(RUN)
algorithm,
renowned
for
its
powerful
optimisation
capabilities,
faces
challenges
in
dealing
with
increasing
complexity
real‐world
problems.
Specifically,
it
shows
deficiencies
terms
of
limited
local
exploration
capabilities
and
less
precise
solutions.
Therefore,
this
research
aims
to
integrate
the
topological
search
(TS)
mechanism
gradient
rule
(GSR)
into
framework
RUN,
introducing
an
enhanced
algorithm
called
TGRUN
improve
performance
original
algorithm.
TS
employs
a
circular
scheme
conduct
thorough
solution
regions
surrounding
each
solution,
enabling
careful
examination
valuable
areas
enhancing
algorithm’s
effectiveness
exploration.
To
prevent
from
becoming
trapped
optima,
GSR
also
integrates
descent
principles
direct
wider
investigation
global
space.
This
study
conducted
serious
experiments
on
IEEE
CEC2017
comprehensive
benchmark
function
assess
TGRUN.
Additionally,
evaluation
includes
engineering
design
feature
selection
problems
serving
as
additional
test
assessing
validation
outcomes
indicate
significant
improvement
accuracy
