Fast-Dynamic Grey Wolf Optimizer for solving model order reduction of bilinear systems based on multi-moment matching technique

Soloklo, Hasan Nasiri; Bigdeli, Nooshin

doi:10.1016/j.asoc.2022.109730

Cited by 5 publications

(3 citation statements)

References 57 publications

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“…In 2022, Soloklo et al proposed an improvement of GWO called Fast-Dynamic Grey Wolf Optimizer [38], which focuses on changing the formulas and control parameters in the original algorithm and adding a mechanism that combines the optimal and worst solutions of each generation according to a certain weight. Singh et al proposed a novel position updating method and control parameters based on the GWO algorithm [39], they will randomly assign weights to alpha, beta and delta, additionally, to adding a Levy-flight based mutation strategy to ensuring the diversity of population species.…”

Section: Related Workmentioning

confidence: 99%

A multi-stage time-backtracking grey wolf optimizer introducing a new hierarchy mechanism

Yang,

2024

Preprint

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The Grey Wolf Optimizer (GWO) is a swarm intelligence algorithm that mimics natural creatures. It is characterized by fast convergence, few parameters and high accuracy. However, after extensive research, it is found that GWO is easily converged too early in the process of the algorithm, which leads to the reduction of population diversity and the results are not accurate enough, in addition, GWO has a problem that the results tend to converge to 0 easily, which means that the algorithm obtains exceptionally good results on the test function with the optimal point of 0, but not in other cases. To solve the main problems of GWO, this paper proposes to introduce a new hierarchical multi-stage adaptive search grey wolf optimization algorithm called MTBGWO. In this paper, we compare MTBGWO with other excellent algorithms on 23 benchmark standard test sets before and after panning and CEC2013 conference test sets, and the results show that MTBGWO can largely improve the above mentioned problems and improve the performance of the algorithm. To further test the usability of the algorithm on real-world problems, the algorithm is used in this paper on three classical engineering problems and the results show that the algorithm has good applicability and accuracy on real-world problems.

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Section: Related Workmentioning

confidence: 99%

A multi-stage time-backtracking grey wolf optimizer introducing a new hierarchy mechanism

Yang,

2024

Preprint

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“…Theorem 2. The Volterra series of Equation ( 12) is uniformly convergent on the interval [0, ∞) for all bounded input if (i) A is stable, (ii) N < 𝛼∕Z 𝛽, where 𝛼 and 𝛽 are two positive scalar such that (13) holds and Z ≥ u(t ) [41].…”

Section: Volterra Series Representation and Bibo Stabilitymentioning

confidence: 99%

“…The sign of · indicate the Euclidean norm of vectors or the spectral norm of matrices. Theorem The Volterra series of Equation (12) is uniformly convergent on the interval [0, ∞) for all bounded input if (i) A is stable, (ii)

N &lt; \alpha /Z\beta

, where α and β are two positive scalar such that (13) holds and

Z \ge u( t )

[41]. …”

Section: Preliminariesmentioning

confidence: 99%

H₂model order reduction of bilinear systems via linear matrix inequality approach

Soloklo

Bigdeli

2023

IET Control Theory & Appl

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This paper proposes an H2‐optimal model order reduction (MOR) method for bilinear systems based on the linear matrix inequality (LMI) approach. In this method, to reduce the computational complexity, at first, a reduced middle‐order approximation of the system is derived based on common bilinear MOR methods. Next, the H2 norm of the error system is minimized to obtain the reduced‐order bilinear model. Generalized Lyapunov equations are added to the optimization problem as LMI constraints to guarantee the specification of type II Gramians of the bilinear system to improve accuracy. Besides, two stability conditions are included to the optimization problem as its constraints to preserve stability of reduced‐order bilinear model. One of advantages of the proposed method is the need for only one of the Gramians of controllability or observability. Since the proposed H2‐optimal MOR problem is a polynomial matrix inequality (PMI) problem, an iterative method is used to convert the PMI to the LMI problems and solve the optimization problem. Three bilinear test systems are considered to show the proposed method's efficiency, while its performance is compared with some classical methods. Results show that the proposed methods lead to a more accurate reduced‐order model than other MOR methods.

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Moth Flame Optimization for Model Order Reduction of Complex High Order Linear Time-Invariant Systems

Goel,

Manocha

2024

Circuits Syst Signal Process

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Fast-Dynamic Grey Wolf Optimizer for solving model order reduction of bilinear systems based on multi-moment matching technique

Cited by 5 publications

References 57 publications

A multi-stage time-backtracking grey wolf optimizer introducing a new hierarchy mechanism

A multi-stage time-backtracking grey wolf optimizer introducing a new hierarchy mechanism

H₂model order reduction of bilinear systems via linear matrix inequality approach

Moth Flame Optimization for Model Order Reduction of Complex High Order Linear Time-Invariant Systems

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Fast-Dynamic Grey Wolf Optimizer for solving model order reduction of bilinear systems based on multi-moment matching technique

Cited by 5 publications

References 57 publications

A multi-stage time-backtracking grey wolf optimizer introducing a new hierarchy mechanism

A multi-stage time-backtracking grey wolf optimizer introducing a new hierarchy mechanism

H2model order reduction of bilinear systems via linear matrix inequality approach

Moth Flame Optimization for Model Order Reduction of Complex High Order Linear Time-Invariant Systems

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H₂model order reduction of bilinear systems via linear matrix inequality approach