Design of Aquila Optimization Heuristic for Identification of Control Autoregressive Systems

Mehmood, Khizer; Chaudhary, Naveed Ishtiaq; Khan, Zeshan Aslam; Raja, Muhammad Asif Zahoor; Cheema, Khalid Mehmood; Milyani, Ahmad H.

doi:10.3390/math10101749

Cited by 25 publications

(7 citation statements)

References 54 publications

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“…In their study, Mehmood et al [ 18 ] employed the AO technique to estimate the control autoregressive (CAR) model’s parameters. To boost the AO’s efficiency, raising the population and the number of generations is done at the expense of the computation complexity.…”

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

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Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.

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Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

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show abstract

“…In [ 86 ], multi-innovation fractional least mean squares were used in estimation. In [ 87 ], an Aquila optimizer is used in parameter estimation of the ARX model, In [ 88 ], Kalman filter-based multi-step length gradient iterative algorithm with missing outputs is used for parameter estimation of the ARX models. In [ 89 ], a Renyi square error entropy and fourth-order statistic of the error–kurtosis–into the variable step size input for used ARX model identification.…”

Section: Introductionmentioning

confidence: 99%

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

et al. 2023

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In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.

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“…Mostly, metaheuristic algorithms are used to solve real-world engineering problems. For instance, non-linear MPA is used for Hammerstein autoregressive exogenous systems 80 , chaotic GWO is used in identification of control autoregressive model 81 , MPA with key term separation technique used for nonlinear Hammerstein system identification 82 , Dwarf mongoose algorithm for identification of autoregressive exogenous model 83 , Aquila optimizer for control autoregressive systems identification 84 , etc. Though the algorithms are used for real-world applications, still most of the algorithms struggle to handle constraint optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems

Ravichandran,

Manoharan,

Jangir

et al. 2023

Sci Rep

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The primary objective of this study is to delve into the application and validation of the Resistance Capacitance Optimization Algorithm (RCOA)—a new, physics-inspired metaheuristic optimization algorithm. The RCOA, intriguingly inspired by the time response of a resistance–capacitance circuit to a sudden voltage fluctuation, has been earmarked for solving complex numerical and engineering design optimization problems. Uniquely, the RCOA operates without any control/tunable parameters. In the first phase of this study, we evaluated the RCOA's credibility and functionality by deploying it on a set of 23 benchmark test functions. This was followed by thoroughly examining its application in eight distinct constrained engineering design optimization scenarios. This methodical approach was undertaken to dissect and understand the algorithm's exploration and exploitation phases, leveraging standard benchmark functions as the yardstick. The principal findings underline the significant effectiveness of the RCOA, especially when contrasted against various state-of-the-art algorithms in the field. Beyond its apparent superiority, the RCOA was put through rigorous statistical non-parametric testing, further endorsing its reliability as an innovative tool for handling complex engineering design problems. The conclusion of this research underscores the RCOA's strong performance in terms of reliability and precision, particularly in tackling constrained engineering design optimization challenges. This statement, derived from the systematic study, strengthens RCOA's position as a potentially transformative tool in the mathematical optimization landscape. It also paves the way for further exploration and adaptation of physics-inspired algorithms in the broader realm of optimization problems.

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Design of Aquila Optimization Heuristic for Identification of Control Autoregressive Systems

Cited by 25 publications

References 54 publications

A Comprehensive Survey on Aquila Optimizer

A Comprehensive Survey on Aquila Optimizer

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems

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