Advances in computer and electrical engineering book series,
Год журнала:
2024,
Номер
unknown, С. 283 - 312
Опубликована: Дек. 6, 2024
The
integration
of
Cyber-Physical
Systems
(CPS)
into
critical
infrastructure
demands
optimization
techniques
that
ensure
both
high
performance
and
privacy
preservation.
This
paper
presents
the
Privacy-Preserving
Hybrid
Bee-Evolutionary
Optimization
Algorithm
(PP-BEOA),
a
novel
variant
nature-inspired
tailored
for
CPS
applications.
PP-BEOA
synergizes
exploratory
capabilities
Artificial
Bee
Colony
(ABC)
algorithms
with
exploitative
strength
Genetic
Algorithms
(GA),
enhanced
by
advanced
differential
mechanisms
secure
multi-party
computation
to
safeguard
sensitive
data.
Machine
learning-driven
parameter
adjustments
further
improve
adaptability
robustness
in
dynamic
environments.
Comprehensive
evaluations
demonstrate
effectiveness
PP-BEOA,
showcasing
superior
results
scalability,
real-time
optimization,
resilience
compared
traditional
approaches.
affirm
PP-BEOA's
potential
as
transformative
approach
addressing
complex
challenges.
Cloud
computing
allows
web-based
services
to
use
a
variety
of
reasonably
priced
or
resources,
removing
the
requirements
centralized
knowledge
access.
There
are
many
provocations
available
in
cloud
and
multi-cloud,
so
bio-inspired
algorithms
frequently
used
handle
particular
problems
such
as
load
imbalance,
resource
equipping,
performance
optimization.
Bio-inspired
have
proclivity
for
spontaneously
resolving
wide
range
challenges
by
giving
optimum
solutions.
The
ability
has
an
impact
on
how
tackle
crucial
issues
computing.
This
study
presents
comprehensive
review
methods
optimizing
solutions
multi-cloud.
ACO,
GA,
PSO,
FPA,
BA
COSMIC
bio
inspired
techniques
considered
based
cost,
scalability,
fault
tolerance,
security,
energy
consumption,
throughput.
ACO
is
secure
having
capability
PSO
fast
but
less
costly
than
others.
Thus,
FPA
better
others
various
parameters.
tolerance
consumption
Intellectual Technologies on Transport,
Год журнала:
2024,
Номер
0(1), С. 5 - 11
Опубликована: Апрель 14, 2024
This
paper
proposes
a
novel
approach
to
solve
complex
industrial
big
data
management
problems
using
genetic
algorithms
(GA),
particle
swarm
optimization
(PSO),
ant
(ACO)
and
cultural
(CA).
The
research
aims
at
efficient
resource
allocation,
balancing
conflicting
objectives
such
as
cost
minimization,
utilization
quality
improvement.
proposed
offers
comprehensive
framework
that
combines
the
advantages
of
different
techniques,
providing
decision
makers
with
important
insights
into
optimal
strategies
in
their
industries.
results
study
show
effectiveness
hybrid
achieving
decisions,
which
improves
operational
efficiency
strategic
making
era
data.
Advances in computer and electrical engineering book series,
Год журнала:
2024,
Номер
unknown, С. 283 - 312
Опубликована: Дек. 6, 2024
The
integration
of
Cyber-Physical
Systems
(CPS)
into
critical
infrastructure
demands
optimization
techniques
that
ensure
both
high
performance
and
privacy
preservation.
This
paper
presents
the
Privacy-Preserving
Hybrid
Bee-Evolutionary
Optimization
Algorithm
(PP-BEOA),
a
novel
variant
nature-inspired
tailored
for
CPS
applications.
PP-BEOA
synergizes
exploratory
capabilities
Artificial
Bee
Colony
(ABC)
algorithms
with
exploitative
strength
Genetic
Algorithms
(GA),
enhanced
by
advanced
differential
mechanisms
secure
multi-party
computation
to
safeguard
sensitive
data.
Machine
learning-driven
parameter
adjustments
further
improve
adaptability
robustness
in
dynamic
environments.
Comprehensive
evaluations
demonstrate
effectiveness
PP-BEOA,
showcasing
superior
results
scalability,
real-time
optimization,
resilience
compared
traditional
approaches.
affirm
PP-BEOA's
potential
as
transformative
approach
addressing
complex
challenges.
