Journal of Engineering Research,
Journal Year:
2023,
Volume and Issue:
unknown
Published: Nov. 1, 2023
In
the
animal
kingdom,
a
mutually-beneficial
ecosystemic
coexistence
and
partnership
in
predation
between
wolves
ravens,
known
as
wolf-bird
relationship,
is
observed
various
cultures.
The
Wolf-Bird
Optimizer
(WBO),
novel
metaheuristic
algorithm
inspired
by
this
natural
zoological
proposed.
This
method
developed
based
on
foraging
behaviors
of
ravens
wolves,
wherein
intelligence
finding
prey
sending
signals
to
for
assistance
hunting
considered.
Furthermore,
framework
resource
tradeoffs
project
scheduling
using
algorithms
Building
Information
Modeling
(BIM)
approach
established
research.
For
statistical
analysis,
are
independently
run
30
times
with
preset
stopping
condition,
enabling
calculation
descriptive
metrics
such
mean,
standard
deviation
(SD),
required
number
objective
function
evaluations.
To
ensure
significance
results,
several
inferential
methods,
including
Kolmogorov-Smirnov,
Wilcoxon,
Mann-Whitney,
Kruskal-Wallis
tests,
employed.
Additionally,
capability
proposed
solving
tradeoff
problems
four
construction
projects
assessed.
performance
WBO
also
evaluated
two
benchmark
projects,
results
indicating
algorithm's
ability
produce
competitive
exceptional
outcomes
regarding
tradeoffs.
PLoS ONE,
Journal Year:
2023,
Volume and Issue:
18(1), P. e0280006 - e0280006
Published: Jan. 3, 2023
Monkey
king
evolution
(MKE)
is
a
population-based
differential
evolutionary
algorithm
in
which
the
single
strategy
and
control
parameter
affect
convergence
balance
between
exploration
exploitation.
Since
strategies
have
considerable
impact
on
performance
of
algorithms,
collaborating
multiple
can
significantly
enhance
abilities
algorithms.
This
our
motivation
to
propose
multi-trial
vector-based
monkey
named
MMKE.
It
introduces
novel
best-history
trial
vector
producer
(BTVP)
random
(RTVP)
that
effectively
collaborate
with
canonical
MKE
(MKE-TVP)
using
approach
tackle
various
real-world
optimization
problems
diverse
challenges.
expected
proposed
MMKE
improve
global
search
capability,
strike
exploitation,
prevent
original
from
converging
prematurely
during
process.
The
was
assessed
CEC
2018
test
functions,
results
were
compared
eight
metaheuristic
As
result
experiments,
it
demonstrated
capable
producing
competitive
superior
terms
accuracy
rate
comparison
comparative
Additionally,
Friedman
used
examine
gained
experimental
statistically,
proving
Furthermore,
four
engineering
design
optimal
power
flow
(OPF)
problem
for
IEEE
30-bus
system
are
optimized
demonstrate
MMKE's
real
applicability.
showed
handle
difficulties
associated
able
solve
multi-objective
OPF
better
solutions
than
Scientific Reports,
Journal Year:
2023,
Volume and Issue:
13(1)
Published: April 1, 2023
In
this
paper,
Squid
Game
Optimizer
(SGO)
is
proposed
as
a
novel
metaheuristic
algorithm
inspired
by
the
primary
rules
of
traditional
Korean
game.
game
multiplayer
with
two
objectives:
attackers
aim
to
complete
their
goal
while
teams
try
eliminate
each
other,
and
it
usually
played
on
large,
open
fields
no
set
guidelines
for
size
dimensions.
The
playfield
often
shaped
like
squid
and,
according
historical
context,
appears
be
around
half
standard
basketball
court.
mathematical
model
developed
based
population
solution
candidates
random
initialization
process
in
first
stage.
are
divided
into
groups
offensive
defensive
players
player
goes
among
start
fight
which
modeled
through
movement
toward
players.
By
considering
winning
states
both
sides
calculated
objective
function,
position
updating
conducted
new
vectors
produced.
To
evaluate
effectiveness
SGO
algorithm,
25
unconstrained
test
functions
100
dimensions
used,
alongside
six
other
commonly
used
metaheuristics
comparison.
independent
optimization
runs
algorithms
pre-determined
stopping
condition
ensure
statistical
significance
results.
Statistical
metrics
such
mean,
deviation,
mean
required
function
evaluations
calculated.
provide
more
comprehensive
analysis,
four
prominent
tests
including
Kolmogorov-Smirnov,
Mann-Whitney,
Kruskal-Wallis
used.
Meanwhile,
ability
suggested
SGOA
assessed
cutting-edge
real-world
problems
newest
CEC
2020,
demonstrate
outstanding
performance
dealing
these
complex
problems.
overall
assessment
indicates
that
can
competitive
remarkable
outcomes
benchmark
Neural Computing and Applications,
Journal Year:
2023,
Volume and Issue:
35(20), P. 14973 - 15004
Published: April 2, 2023
Abstract
The
Chaos
Game
Optimization
(CGO)
has
only
recently
gained
popularity,
but
its
effective
searching
capabilities
have
a
lot
of
potential
for
addressing
single-objective
optimization
issues.
Despite
advantages,
this
method
can
tackle
problems
formulated
with
one
objective.
multi-objective
CGO
proposed
in
study
is
utilized
to
handle
the
several
objectives
(MOCGO).
In
MOCGO,
Pareto-optimal
solutions
are
stored
fixed-sized
external
archive.
addition,
leader
selection
functionality
needed
carry
out
been
included
CGO.
technique
also
applied
eight
real-world
engineering
design
challenges
multiple
objectives.
MOCGO
algorithm
uses
mathematical
models
chaos
theory
and
fractals
inherited
from
This
algorithm's
performance
evaluated
using
seventeen
case
studies,
such
as
CEC-09,
ZDT,
DTLZ.
Six
well-known
algorithms
compared
four
different
metrics.
results
demonstrate
that
suggested
better
than
existing
ones.
These
show
excellent
convergence
coverage.
