Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(S3), P. 3205 - 3271
Published: Oct. 3, 2023
Language: Английский
Mathematics and Computers in Simulation, Journal Year: 2023, Volume and Issue: 212, P. 195 - 223
Published: May 6, 2023
Language: Английский
Citations
24
Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(12)
Published: Oct. 17, 2024
Language: Английский
Citations
10
Mechanical Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 209, P. 111118 - 111118
Published: Jan. 18, 2024
Language: Английский
Citations
9
Journal of Computational Design and Engineering, Journal Year: 2022, Volume and Issue: 10(1), P. 329 - 356
Published: Dec. 14, 2022
Abstract The African vultures optimization algorithm (AVOA) is a recently proposed metaheuristic inspired by the vultures’ behaviors. Though basic AVOA performs very well for most problems, it still suffers from shortcomings of slow convergence rate and local optimal stagnation when solving complex tasks. Therefore, this study introduces modified version named enhanced (EAVOA). EAVOA uses three different techniques namely representative vulture selection strategy, rotating flight selecting accumulation mechanism, respectively, which are developed based on AVOA. strategy strikes good balance between global searches. mechanism utilized to improve quality solution. performance validated 23 classical benchmark functions with various types dimensions compared those nine other state-of-the-art methods according numerical results curves. In addition, real-world engineering design problems adopted evaluate practical applicability EAVOA. Furthermore, has been applied classify multi-layer perception using XOR cancer datasets. experimental clearly show that superiority over methods.
Language: Английский
Citations
30
Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 236, P. 121413 - 121413
Published: Sept. 3, 2023
Language: Английский
Citations
19
Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(3), P. 12 - 42
Published: April 10, 2024
Abstract In recent years, scholars have developed and enhanced optimization algorithms to tackle high-dimensional engineering challenges. The primary challenge of lies in striking a balance between exploring wide search space focusing on specific regions. Meanwhile, design problems are intricate come with various constraints. This research introduces novel approach called Hippo Swarm Optimization (HSO), inspired by the behavior hippos, designed address real-world HSO encompasses four distinct strategies based hippos different scenarios: starvation search, alpha margination, competition. To assess effectiveness HSO, we conducted experiments using CEC2017 test set, featuring highest dimensional problems, CEC2022 constrained problems. parallel, employed 14 established as control group. experimental outcomes reveal that outperforms well-known algorithms, achieving first average ranking out them CEC2022. Across classical consistently delivers best results. These results substantiate highly effective algorithm for both
Language: Английский
Citations
6
Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(3), P. 1659 - 1700
Published: Nov. 30, 2023
Language: Английский
Citations
13
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 36908 - 36923
Published: Jan. 1, 2024
Aiming
at
the
problem
of
noise
interference
leading
to
poor
fault
diagnosis
effect
rolling
bearing,
a
two-stage
signal
reduction
method
based
on
multi-strategy
coati
optimization
algorithm
(MFCOA)
optimized
ICEEMDAN
combined
with
wavelet
threshold
denoising
(ICEEMDAN-WTD)
is
proposed.
The
MFCOA-optimized
stochastic
configured
networks
(MFCOA-SCNs)
used
for
type
identification.
Firstly,
decompose
noisy
signals
into
several
IMF
signals,
and
then
process
whose
arrangement
entropy
lower
than
pre-set
value
obtain
reconstructed
signals.
In
this
process,
decomposition
affected
by
number
white
additions
degree
addition.
Therefore,
MFCOA
introduced
optimize
parameters
ensure
reduction.
Secondly,
applied
signal,
samples
each
order
calculated
characteristics
in
different
simultaneous
frequency
domains.
Finally,
scale
factor
λ
regularization
coefficient
Language: Английский
Citations
4
Electronics, Journal Year: 2024, Volume and Issue: 13(16), P. 3329 - 3329
Published: Aug. 22, 2024
This paper proposes an improved African vulture optimization algorithm (IROAVOA), which integrates the random opposition-based learning strategy and disturbance factor to solve problems such as relatively weak global search capability poor ability balance exploration exploitation stages. IROAVOA is divided into two parts. Firstly, introduced in population initialization stage improve diversity of population, enabling more comprehensively explore potential solution space convergence speed algorithm. Secondly, at increase randomness algorithm, effectively avoiding falling local optimal allowing a better To verify effectiveness proposed comprehensive testing was conducted using 23 benchmark test functions, CEC2019 suite, engineering problems. The compared with seven state-of-the-art metaheuristic algorithms experiments five experiments. experimental results indicate that achieved mean values all functions significant improvement speed. It can also than other algorithms.
Language: Английский
Citations
4
Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126532 - 126532
Published: Jan. 1, 2025
Language: Английский
Citations
0