Economic
dispatch
study
is
important
in
the
electric
power
industry
because
it
concerned
with
efficient
electrical
production
and
economics.
It
crucial
to
reduce
operating
costs
of
energy
even
small
savings
have
a
large
impact
on
total
generation
fuel
consumption.
This
paper
presents
proposed
algorithm
namely
Hybrid
Evolutionary-Barnacles
Mating
Optimization
(HEBMO)
solve
non-convex
economic
(ED)
problems
specifically
under
line
generator
outages.
The
evaluation
tested
two
types
reliability
test
systems
(RTS),
named
IEEE
30-Bus
RTS
57-Bus
RTS.
HEBMO
compared
single
optimization
algorithm,
EP
BMO
for
performance
purposes.
results
show
that
outperforms
terms
minimizing
cost.
On
other
hand,
also
achieves
convincing
fast
computational
time.
AIP Advances,
Journal Year:
2022,
Volume and Issue:
12(9)
Published: Sept. 1, 2022
An
improved
optimization
algorithm,
namely,
multi-strategy-sparrow
search
algorithm
(MSSSA),
is
proposed
to
solve
highly
non-linear
problems.
In
MSSSA,
a
circle
map
utilized
improve
the
quality
of
population.
Moreover,
adaptive
survival
escape
strategy
(ASES)
enhance
ability
sparrows.
producer
stage,
craziness
factor
integrated
with
ASES
introduced
accuracy
and
ability.
scout
facilitates
sparrows
successful
from
danger.
Besides,
opposition-based
learning
or
Gaussian–Chachy
variation
helps
optimal
individuals
local
solutions.
The
performance
MSSSA
investigated
on
well-known
23
basic
functions
CEC2014
test
suite.
Furthermore,
applied
optimize
real-life
engineering
results
show
that
presents
excellent
feasibility
practicality
compared
other
state-of-the-art
algorithms.
Slime
Mould
Algorithm
(SMA)
is
a
new
swarm
intelligence
algorithm
inspired
by
the
oscillatory
behavior
of
slime
molds
during
foraging.
Numerous
researchers
have
widely
applied
SMA
and
its
variants
in
various
domains
proved
value
experiments
literatures.
In
this
paper
comprehensive
survey
on
introduced,
which
based
130
articles
visa
Google-scholar
between
2022
July,
2023.
Firstly,
theory
described.
Secondly
improved
are
provided
categorized
according
to
approach
that
they
with.
Finally,
it
also
discusses
main
applications
such
as
engineering
optimization,
energy
machine
learning,
network,
scheduling
image
segmentation
etc.
This
review
presents
some
research
suggestion
for
researcher
who
interested
algorithm.
The
standard
Slime
Mould
Algorithm
has
problems
such
as
falling
into
local
optimal
traps,
slow
convergence
speed
and
low
precision.
In
order
to
improve
the
performance
of
algorithm,
a
new
Multi-strategy
Fusion
based
(MFSMA)
was
proposed.
MFSMA,
slime
mould
population
initialized
with
singer
chaotic
mapping
evenly
distributed
in
search
space,
global
ability
improved
by
alternating
between
short
distance
occasionally
longer
walk
Levy-flight
mechanism,
nonlinear
factor
proposed
balance
exploration
development
algorithm.
algorithm
able
find
more
precise
parameters
various
optimization
than
conventional
one.
During
test
phase,
comparison
conducted
on
MFSMA
other
three
functions.
results
indicated
that
had
better
ability,
faster
higher
solving
accuracy.
Economic
dispatch
study
is
important
in
the
electric
power
industry
because
it
concerned
with
efficient
electrical
production
and
economics.
It
crucial
to
reduce
operating
costs
of
energy
even
small
savings
have
a
large
impact
on
total
generation
fuel
consumption.
This
paper
presents
proposed
algorithm
namely
Hybrid
Evolutionary-Barnacles
Mating
Optimization
(HEBMO)
solve
non-convex
economic
(ED)
problems
specifically
under
line
generator
outages.
The
evaluation
tested
two
types
reliability
test
systems
(RTS),
named
IEEE
30-Bus
RTS
57-Bus
RTS.
HEBMO
compared
single
optimization
algorithm,
EP
BMO
for
performance
purposes.
results
show
that
outperforms
terms
minimizing
cost.
On
other
hand,
also
achieves
convincing
fast
computational
time.
