Water,
Journal Year:
2021,
Volume and Issue:
13(5), P. 644 - 644
Published: Feb. 28, 2021
Every
morning,
water
suppliers
need
to
define
their
pump
schedules
for
the
next
24
h
drinking
production.
Plans
must
be
designed
in
such
a
way
that
is
always
available
and
amount
of
unused
pumped
into
network
reduced.
Therefore,
operators
accurately
estimate
day’s
consumption
profile.
In
real-life
applications
with
standard
profiles,
some
expert
system
or
vector
autoregressive
models
are
used.
Still,
recent
years,
significant
improvements
time
series
prediction
have
been
achieved
through
special
deep
learning
algorithms
called
long
short-term
memory
(LSTM)
networks.
This
paper
investigates
applicability
LSTM
demand
optimal
control
compares
LSTMs
against
other
methods
currently
used
by
suppliers.
It
shown
outperform
since
they
can
easily
integrate
additional
information
like
day
week
national
holidays.
Furthermore,
online-
transfer-learning
capabilities
investigated.
only
couple
days
training
data
achieve
reasonable
results.
As
focus
on
real-world
application
LSTMs,
from
two
different
distribution
plants
benchmarking.
Finally,
it
significantly
operation.
IEEE Transactions on Evolutionary Computation,
Journal Year:
2018,
Volume and Issue:
23(2), P. 303 - 315
Published: July 19, 2018
When
solving
constrained
multiobjective
optimization
problems,
an
important
issue
is
how
to
balance
convergence,
diversity,
and
feasibility
simultaneously.
To
address
this
issue,
paper
proposes
a
parameter-free
constraint
handling
technique,
two-archive
evolutionary
algorithm,
for
optimization.
It
maintains
two
collaborative
archives
simultaneously:
one,
denoted
as
the
convergence-oriented
archive
(CA),
driving
force
push
population
toward
Pareto
front;
other
diversity-oriented
(DA),
mainly
tends
maintain
diversity.
In
particular,
complement
behavior
of
CA
provide
much
diversified
information
possible,
DA
aims
at
exploring
areas
under-exploited
by
including
infeasible
regions.
leverage
complementary
effects
both
archives,
we
develop
restricted
mating
selection
mechanism
that
adaptively
chooses
appropriate
parents
from
them
according
their
evolution
status.
Comprehensive
experiments
on
series
benchmark
problems
real-world
case
study
fully
demonstrate
competitiveness
our
proposed
in
comparison
five
state-of-the-art
optimizers.
IEEE Transactions on Evolutionary Computation,
Journal Year:
2020,
Volume and Issue:
25(1), P. 102 - 116
Published: June 22, 2020
Constrained
multiobjective
optimization
problems
(CMOPs)
are
challenging
because
of
the
difficulty
in
handling
both
multiple
objectives
and
constraints.
While
some
evolutionary
algorithms
have
demonstrated
high
performance
on
most
CMOPs,
they
exhibit
bad
convergence
or
diversity
CMOPs
with
small
feasible
regions.
To
remedy
this
issue,
article
proposes
a
coevolutionary
framework
for
constrained
optimization,
which
solves
complex
CMOP
assisted
by
simple
helper
problem.
The
proposed
evolves
one
population
to
solve
original
another
problem
derived
from
one.
two
populations
evolved
same
optimizer
separately,
assistance
solving
is
achieved
sharing
useful
information
between
populations.
In
experiments,
compared
several
state-of-the-art
tailored
CMOPs.
High
competitiveness
applying
it
47
benchmark
vehicle
routing
time
windows.
IEEE Transactions on Evolutionary Computation,
Journal Year:
2022,
Volume and Issue:
27(2), P. 201 - 221
Published: March 1, 2022
Handling
constrained
multiobjective
optimization
problems
(CMOPs)
is
extremely
challenging,
since
multiple
conflicting
objectives
subject
to
various
constraints
require
be
simultaneously
optimized.
To
deal
with
CMOPs,
numerous
evolutionary
algorithms
(CMOEAs)
have
been
proposed
in
recent
years,
and
they
achieved
promising
performance.
However,
there
has
few
literature
on
the
systematic
review
of
related
studies
currently.
This
article
provides
a
comprehensive
survey
for
optimization.
We
first
large
number
CMOEAs
through
categorization
analyze
their
advantages
drawbacks
each
category.
Then,
we
summarize
benchmark
test
investigate
performance
different
constraint
handling
techniques
(CHTs)
algorithms,
followed
by
some
emerging
representative
applications
CMOEAs.
Finally,
discuss
new
challenges
point
out
directions
future
research
field
Annual Reviews in Control,
Journal Year:
2023,
Volume and Issue:
55, P. 442 - 465
Published: Jan. 1, 2023
Model
Predictive
Control
(MPC)
has
recently
gained
increasing
interest
in
the
adaptive
management
of
water
resources
systems
due
to
its
capability
incorporating
disturbance
forecasts
into
real-time
optimal
control
problems.
Yet,
related
literature
is
scattered
with
heterogeneous
applications,
case-specific
problem
settings,
and
results
that
are
hardly
generalized
transferable
across
systems.
Here,
we
systematically
review
149
peer-reviewed
journal
articles
published
over
last
25
years
on
MPC
applied
reservoirs,
open
channels,
urban
networks
identify
common
trends
challenges
research
practice.
The
three
consider
inter-connected,
multi-purpose
multi-scale
dynamical
affected
by
multiple
hydro-climatic
uncertainties
evolving
socioeconomic
factors.
Our
first
identifies
four
main
currently
limiting
most
applications
domain:
(i)
lack
systematic
benchmarking
respect
other
methods;
(ii)
assessment
impact
model-based
control;
(iii)
limited
analysis
diverse
forecast
types,
resolutions,
prediction
horizons;
(iv)
under-consideration
multi-objective
nature
We
then
argue
future
should
focus
addressing
these
as
key
priorities
for
developments.
ACS Infectious Diseases,
Journal Year:
2024,
Volume and Issue:
10(3), P. 808 - 826
Published: Feb. 28, 2024
Recent
pandemics,
including
the
COVID-19
outbreak,
have
brought
up
growing
concerns
about
transmission
of
zoonotic
diseases
from
animals
to
humans.
This
highlights
requirement
for
a
novel
approach
discern
and
address
escalating
health
threats.
The
One
Health
paradigm
has
been
developed
as
responsive
strategy
confront
forthcoming
outbreaks
through
early
warning,
highlighting
interconnectedness
humans,
animals,
their
environment.
system
employs
several
innovative
methods
such
use
advanced
technology,
global
collaboration,
data-driven
decision-making
come
with
an
extraordinary
solution
improving
worldwide
disease
responses.
Review
deliberates
environmental,
animal,
human
factors
that
influence
risk,
analyzes
challenges
advantages
inherent
in
using
surveillance
system,
demonstrates
how
these
can
be
empowered
by
Big
Data
Artificial
Intelligence.
Holistic
Surveillance
Framework
presented
herein
holds
potential
revolutionize
our
capacity
monitor,
understand,
mitigate
impact
infectious
on
populations.
Water,
Journal Year:
2018,
Volume and Issue:
10(3), P. 307 - 307
Published: March 13, 2018
Optimisation
of
water
distribution
system
design
is
a
well-established
research
field,
which
has
been
extremely
productive
since
the
end
1980s.
Its
primary
focus
to
minimise
cost
proposed
pipe
network
infrastructure.
This
paper
reviews
in
systematic
manner
articles
published
over
past
three
decades,
are
relevant
new
systems,
and
strengthening,
expansion
rehabilitation
existing
inclusive
timing,
parameter
uncertainty,
quality,
operational
considerations.
It
identifies
trends
limits
provides
future
directions.
Exclusively,
this
review
also
contains
comprehensive
information
from
one
hundred
twenty
publications
tabular
form,
including
optimisation
model
formulations,
solution
methodologies
used,
other
important
details.
