Research Square (Research Square),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Feb. 16, 2024
Abstract
Rime
optimization
algorithm
(RIME)
is
an
emerging
metaheuristic
algorithm.
However,
RIME
encounters
issues
such
as
imbalance
between
exploitation
and
exploration,
susceptibility
to
local
optima,
low
convergence
accuracy
when
handling
problems.
To
address
these
drawbacks,
this
paper
introduces
a
variant
of
called
IRIME.
IRIME
integrates
the
soft
besiege
(SB)
composite
mutation
strategy
restart
(CMS-RS),
aiming
balance
exploration
in
RIME,
enhance
population
diversity,
improve
accuracy,
endow
with
capability
escape
optima.
comprehensively
validate
IRIME's
performance,
IEEE
CEC
2017
benchmark
tests
were
conducted,
comparing
it
against
13
conventional
algorithms
11
advanced
algorithms,
including
excellent
competition
JADE.
The
results
indicate
that
performance
best.
practical
applicability,
proposes
binary
version,
bIRIME,
applied
feature
selection
bIRIMR
performs
well
on
12
low-dimensional
datasets
24
high-dimensional
datasets.
It
outperforms
other
terms
number
subsets
classification
accuracy.
In
conclusion,
bIRIME
notably
selection,
particularly
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Feb. 25, 2025
This
paper
addresses
issues
of
inadequate
accuracy
and
inconsistency
between
global
search
efficacy
local
development
capability
in
the
black-winged
kite
algorithm
for
practical
problem-solving
by
proposing
a
optimization
that
integrates
Osprey
Crossbar
enhancement
(DKCBKA).
Firstly,
adaptive
index
factor
fusion
Optimization
Algorithm
approach
are
incorporated
to
enhance
algorithm's
convergence
rate,
probability
distribution
is
updated
throughout
attack
stage.
Second,
stochastic
difference
variant
method
implemented
prevent
from
entering
optima.
Lastly,
longitudinal
transversal
crossover
technique
dynamically
alter
population's
individual
optimal
solutions.
Fifteen
benchmark
functions
chosen
test
effectiveness
enhanced
compare
efficiency
each
technique.
Simulation
experiments
performed
on
CEC2017
CEC2019
sets,
revealing
DKCBKA
surpasses
five
standard
swarm
intelligence
methods
six
improved
algorithms
regarding
solution
speed.
The
superiority
meeting
real
challenges
further
demonstrated
three
engineering
problems
DKCBKA,
with
capabilities
18.222%,
99.885%
0.561%
higher
than
BKA,
respectively.
Engineering Computations,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 18, 2024
Purpose
Most
of
the
existing
time-cost-quality-environmental
impact
trade-off
(TCQET)
analysis
models
have
focused
on
solving
a
simple
project
representation
without
taking
typical
activity
and
characteristics
into
account.
This
study
aims
to
present
novel
approach
called
“hybrid
opposition
learning-based
Aquila
Optimizer”
(HOLAO)
for
optimizing
TCQET
decisions
in
generalized
construction
projects.
Design/methodology/approach
In
this
paper,
HOLAO
algorithm
is
designed,
incorporating
quasi-opposition-based
learning
(QOBL)
quasi-reflection-based
(QRBL)
strategies
initial
population
generation
jumping
phases,
respectively.
The
crowded
distance
rank
(CDR)
mechanism
utilized
optimal
Pareto-front
solutions
assist
decision-makers
(DMs)
achieving
single
compromise
solution.
Findings
efficacy
proposed
methodology
evaluated
by
examining
problems,
involving
69
290
activities,
Results
indicate
that
provides
competitive
problems
It
observed
surpasses
multiple
objective
social
group
optimization
(MOSGO),
plain
Optimization
(AO),
QRBL
QOBL
algorithms
terms
both
number
function
evaluations
(NFE)
hypervolume
(HV)
indicator.
Originality/value
paper
introduces
concept
hybrid
opposition-based
(HOL),
which
incorporates
two
strategies:
as
an
explorative
exploitative
opposition.
Achieving
effective
balance
between
exploration
exploitation
crucial
success
any
algorithm.
To
end,
are
developed
ensure
proper
equilibrium
phases
basic
AO
third
contribution
provide
resource
utilizations
(construction
plans)
evaluate
these
resources
performance.
iScience,
Journal Year:
2024,
Volume and Issue:
27(8), P. 110561 - 110561
Published: July 22, 2024
Rime
optimization
algorithm
(RIME)
encounters
issues
such
as
an
imbalance
between
exploitation
and
exploration,
susceptibility
to
local
optima,
low
convergence
accuracy
when
handling
problems.
This
paper
introduces
a
variant
of
RIME
called
IRIME
address
these
drawbacks.
integrates
the
soft
besiege
(SB)
composite
mutation
strategy
(CMS)
restart
(RS).
To
comprehensively
validate
IRIME's
performance,
IEEE
CEC
2017
benchmark
tests
were
conducted,
comparing
it
against
many
advanced
algorithms.
The
results
indicate
that
performance
is
best.
In
addition,
applying
in
four
engineering
problems
reflects
solving
practical
Finally,
proposes
binary
version,
bIRIME,
can
be
applied
feature
selection
bIRIMR
performs
well
on
12
low-dimensional
datasets
24
high-dimensional
datasets.
It
outperforms
other
algorithms
terms
number
subsets
classification
accuracy.
conclusion,
bIRIME
has
great
potential
selection.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: June 1, 2024
The
RIME
optimization
algorithm
(RIME)
represents
an
advanced
technique.
However,
it
suffers
from
issues
such
as
slow
convergence
speed
and
susceptibility
to
falling
into
local
optima.
In
response
these
shortcomings,
we
propose
a
multi-strategy
enhanced
version
known
the
improved
(MIRIME).
Firstly,
Tent
chaotic
map
is
utilized
initialize
population,
laying
groundwork
for
global
optimization.
Secondly,
introduce
adaptive
update
strategy
based
on
leadership
dynamic
centroid,
facilitating
swarm's
exploitation
in
more
favorable
direction.
To
address
problem
of
population
scarcity
later
iterations,
lens
imaging
opposition-based
learning
control
introduced
enhance
diversity
ensure
accuracy.
proposed
centroid
boundary
not
only
limits
search
boundaries
individuals
but
also
effectively
enhances
algorithm's
focus
efficiency.
Finally,
demonstrate
performance
MIRIME,
employ
CEC
2017
2022
test
suites
compare
with
11
popular
algorithms
across
different
dimensions,
verifying
its
effectiveness.
Additionally,
assess
method's
practical
feasibility,
apply
MIRIME
solve
three-dimensional
path
planning
unmanned
surface
vehicles.
Experimental
results
indicate
that
outperforms
other
competing
terms
solution
quality
stability,
highlighting
superior
application
potential.
