Optimal model order reduction based on hybridization of adaptive safe experimentation dynamics-nonlinear sine cosine algorithm
Convoluted high-order structures as modeled through mathematical principle including telecommunication systems, power plants for urbanized energy supply and aerospace systems are often accompanied by the apparent setbacks in analyzing, experimentation and operational control. The complexity of...
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Main Authors: | , , , , |
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Format: | Conference or Workshop Item |
Language: | English |
Published: |
2023
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Online Access: | http://eprints.utem.edu.my/id/eprint/28047/1/Optimal%20model%20order%20reduction%20based%20on%20hybridization%20of%20adaptive%20safe%20experimentation%20dynamics-nonlinear%20sine%20cosine%20algorithm.pdf http://eprints.utem.edu.my/id/eprint/28047/ https://ieeexplore.ieee.org/document/10227161 |
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Institution: | Universiti Teknikal Malaysia Melaka |
Language: | English |
Summary: | Convoluted high-order structures as modeled
through mathematical principle including telecommunication
systems, power plants for urbanized energy supply and
aerospace systems are often accompanied by the apparent
setbacks in analyzing, experimentation and operational control.
The complexity of such structures is proposedly decreased
within the current study through introduction of a hybridized
meta-heuristics fine-tuning approach between Adaptive Safe
Experimentation Dynamics (ASED) and Nonlinear Sine Cosine
Algorithm (NSCA). Entrapment within the local optima is
hereby overcome through ASED by adaptive random
perturbation, with improved exploration and exploitation of the
introduced approach being further enabled by NSCA. The
method’s potency was evaluated through an empirically
adopted 6th order numerical function. Experimentation
outcomes uncovered profound robustness and consistency from
ASED-NSCA against alternative modern optimization-based
techniques towards comparatively outstanding model order
reduction (MOR). |
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