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.
International Journal of Systems Science,
Journal Year:
2022,
Volume and Issue:
54(1), P. 204 - 235
Published: Dec. 16, 2022
Slime
Mould
Algorithm
(SMA)
has
recently
received
much
attention
from
researchers
because
of
its
simple
structure,
excellent
optimisation
capabilities,
and
acceptable
convergence
in
dealing
with
various
types
complex
real-world
problems.
this
study
aims
to
retrieve,
identify,
summarise
analyse
critical
studies
related
SMA
development.
Based
on
this,
98
SMA-related
the
Web
Science
were
retrieved,
selected,
identified.
The
two
main
review
vectors
advanced
versions
SMAs
application
domains.
First,
we
counted
analysed
SMAs,
summarised,
classified,
discussed
their
improvement
methods
directions.
Secondly,
sort
out
domains
role,
development
status,
shortcomings
each
domain.
A
survey
based
existing
literature
shows
that
clearly
outperform
some
established
metaheuristics
terms
speed
accuracy
handling
benchmark
problems
solving
multiple
realistic
optimization
This
not
only
suggests
possible
future
directions
field
but,
due
inclusion
graphical
tabular
comparisons
properties,
also
provides
a
comprehensive
source
information
about
SAMs
scope
adaptation
for
Alexandria Engineering Journal,
Journal Year:
2023,
Volume and Issue:
72, P. 573 - 591
Published: April 24, 2023
The
aim
of
the
optimization
economic
load
dispatch
(ELD)
problem
is
to
assign
optimal
generated
power
thermal
units
for
cost
reduction
with
satisfying
loading
operational
constraints.
ELD
a
high-dimensional
and
non-convex
that
became
more
complex
in
case
optimizing
output
large-scale
systems.
In
this
regard,
an
enhanced
version
Beluga
whale
(EBWO)
proposed
deal
(BWO)
efficient
new
technique
mimics
behavior
whales
(BWs)
preying,
swimming,
fall.
However,
BWO
may
suffer
from
stagnation
local
optima
scarcity
population
diversity
like
other
metaheuristics.
EBWO
algorithm
presented
render
standard
robust
powerful
search
by
using
two
strategies
including
cyclone
foraging
motion
boosting
exploitation
phase
quasi-oppositional
based
learning
(QOBL)
improving
diversity.
Firstly,
Simulations
are
carried
out
on
seven
benchmark
functions
prove
validation
algorihm
compared
five
recent
algorithms.
Then,
performance
checked
11-units,
40-units,
also
110-unit
test
systems,
obtained
results
well-known
techniques
such
as
classical
BWO,
FOX
Optimization
Algorithm
(FOX),
Skill
(SOA),
Sand
Cat
swarm
(SCSO)
well
existing
algorithms
literature
DE,
TLBO,
MPSO,
NGWO,
IGA,
NPSO,
CJAYA,
SMA,
PSO,
PPSO,
SSA,
MPA,
MGMPA,
HSSA.
Numerical
show
very
competitive
reported
obtaining
low
fuel
costs.
International Journal of Electrical Power & Energy Systems,
Journal Year:
2023,
Volume and Issue:
156, P. 109719 - 109719
Published: Dec. 15, 2023
The
primary
objective
of
Economic
Load
Dispatch
(ELD)
is
to
determine
the
most
efficient
distribution
power
among
generating
units
while
considering
various
constraints,
such
as
minimum
and
maximum
output,
transmission
line
capacity,
reserve
requirements.
By
solving
ELD
problem,
system
operators
can
minimize
overall
operating
cost
enhance
its
efficiency,
which
has
far-reaching
implications
for
sustainable
energy
management
resource
allocation.
However,
because
non-convex
nature
finding
global
optimum
solution
poses
a
significant
challenge.
Consequently,
several
optimization
techniques,
metaheuristics,
have
been
developed
in
order
address
this
type
problems.
iteratively
exploring
space,
metaheuristics
offer
higher
likelihood
near-optimal
solutions,
even
presence
multiple
local
optima.
This
research
introduces
an
enhanced
social
network
search
(ESNS)
algorithm
improvement
over
existing
(SNS)
algorithm,
aiming
achieve
aforementioned
objectives.
core
SNS
driven
by
users'
dialogue,
imitation,
creativity,
disputation
moods.
proposed
ESNS
builds
upon
approach
enhancing
capability,
particularly
around
best
potential
solution.
goal
improve
algorithm's
ability
explore
possibilities
avoiding
being
trapped
locally
optimal
solutions.
performance
tested
23
benchmark
test
suits,
superiority
against
other
recent
algorithms
verified.
Moreover,
To
evaluate
effectiveness
it
applied
four
standard
systems
comprising
11-,
15-,
40-,
110-unit
systems.
results
demonstrate
that
outperforms
terms
quality
convergence
speed.
These
findings
suggest
holds
promise
valuable
tool
researchers
addressing
economic
dispatch
problem.
Overall,
technique
presents
promising
result
complex
challenges.
Its
capability
handle
constraints
superior
compared
make
addition
set
tools
available
ELD.
Neural Computing and Applications,
Journal Year:
2024,
Volume and Issue:
36(18), P. 10613 - 10635
Published: March 27, 2024
Abstract
This
article
proposes
the
use
of
a
leader
white
shark
optimizer
(LWSO)
with
aim
improving
exploitation
conventional
(WSO)
and
solving
economic
operation-based
load
dispatch
(ELD)
problem.
The
ELD
problem
is
crucial
aspect
power
system
operation,
involving
allocation
generation
resources
to
meet
demand
while
minimizing
operational
costs.
proposed
approach
aims
enhance
performance
efficiency
WSO
by
introducing
leadership
mechanism
within
optimization
process,
which
aids
in
more
effectively
navigating
complex
solution
space.
LWSO
achieves
increased
utilizing
leader-based
mutation
selection
throughout
each
sharks.
efficacy
algorithm
tested
on
13
engineer
benchmarks
non-convex
problems
from
CEC
2020
compared
recent
metaheuristic
algorithms
such
as
dung
beetle
(DBO),
WSO,
fox
(FOX),
moth-flame
(MFO)
algorithms.
also
used
address
different
case
studies
(6
units,
10
11
40
units),
20
separate
runs
using
other
competitive
being
statistically
assessed
demonstrate
its
effectiveness.
results
show
that
outperforms
algorithms,
achieving
best
for
minimum
fuel
cost
Additionally,
statistical
tests
are
conducted
validate
competitiveness
algorithm.
Processes,
Journal Year:
2025,
Volume and Issue:
13(2), P. 405 - 405
Published: Feb. 4, 2025
The
economic
dispatch
(ED)
problem
focuses
on
the
optimal
scheduling
of
thermal
generating
units
in
a
power
system
to
minimize
fuel
costs
while
satisfying
operational
constraints.
This
article
proposes
modified
version
social
group
optimization
(SGO)
algorithm
address
ED
with
various
practical
characteristics
(such
as
valve-point
effects,
transmission
losses,
prohibited
operating
zones,
and
multi-fuel
sources).
SGO
is
population-based
metaheuristic
strong
exploration
capabilities,
but
for
certain
types
problems,
it
may
stagnate
local
optimum
due
potential
imbalance
between
exploitation.
new
version,
named
SGO-L,
retains
structure
incorporates
Laplace
operator
derived
from
distribution
into
all
iterative
solution
update
equations.
adjustment
generates
more
effective
search
steps
space,
improving
exploration–exploitation
balance
overall
performance
terms
stability
quality.
SGO-L
validated
four
systems
small
(six-unit),
medium
(10-unit),
large
(40-unit
110-unit)
sizes
diverse
characteristics.
efficiency
compared
other
algorithms.
experimental
results
demonstrate
that
proposed
robust
than
well-known
algorithms
particle
swarm
optimization,
genetic
algorithms,
differential
evolution,
cuckoo
algorithms)
competitor
mentioned
study.
Moreover,
non-parametric
Wilcoxon
statistical
test
indicates
promising
original
For
example,
standard
deviation
obtained
by
shows
significantly
lower
values
(6.02
×
10−9
USD/h
six-unit
system,
7.56
10−5
10-unit
75.89
40-unit
4.80
10−3
110-unit
system)
(0.44
50.80
274.91
1.04
system).
