Cluster Computing,
Год журнала:
2024,
Номер
27(8), С. 10589 - 10631
Опубликована: Май 8, 2024
Abstract
This
study
introduces
the
Multi-objective
Generalized
Normal
Distribution
Optimization
(MOGNDO)
algorithm,
an
advancement
of
(GNDO)
now
adapted
for
multi-objective
optimization
tasks.
The
GNDO
previously
known
its
effectiveness
in
single-objective
optimization,
has
been
enhanced
with
two
key
features
optimization.
first
is
addition
archival
mechanism
to
store
non-dominated
Pareto
optimal
solutions,
ensuring
a
detailed
record
best
outcomes.
second
enhancement
new
leader
selection
mechanism,
designed
strategically
identify
and
select
solutions
from
archive
guide
process.
positions
MOGNDO
as
cutting-edge
solution
setting
benchmark
evaluating
performance
against
leading
algorithms
field.
algorithm's
rigorously
tested
across
35
varied
case
studies,
encompassing
both
mathematical
engineering
challenges,
benchmarked
prominent
like
MOPSO,
MOGWO,
MOHHO,
MSSA,
MOALO,
MOMVO,
MOAOS.
Utilizing
metrics
such
Generational
Distance
(GD),
Inverted
(IGD),
Maximum
Spread
(MS),
underscores
MOGNDO's
ability
produce
fronts
high
quality,
marked
by
exceptional
precision
diversity.
results
affirm
superior
versatility,
not
only
theoretical
tests
but
also
addressing
complex
real-world
problems,
showcasing
convergence
coverage
capabilities.
source
codes
algorithm
are
publicly
available
at
https://nimakhodadadi.com/algorithms-%2B-codes
.
Mathematical Biosciences & Engineering,
Год журнала:
2022,
Номер
19(12), С. 14173 - 14211
Опубликована: Янв. 1, 2022
<abstract><p>The
Aquila
optimizer
(AO)
is
a
recently
developed
swarm
algorithm
that
simulates
the
hunting
behavior
of
birds.
In
complex
optimization
problems,
an
AO
may
have
slow
convergence
or
fall
in
sub-optimal
regions,
especially
high
ones.
This
paper
tries
to
overcome
these
problems
by
using
three
different
strategies:
restart
strategy,
opposition-based
learning
and
chaotic
local
search.
The
named
as
mAO
was
tested
29
CEC
2017
functions
five
engineering
constrained
problems.
results
prove
superiority
efficiency
solving
many
issues.</p></abstract>
