Reduced order infinite impulse response system identification using manta ray foraging optimization

Mahata, Shibendu; Herencsar, Norbert; Baykant Alagoz, Baris; Yeroglu, Celaleddin

doi:10.1016/j.aej.2023.12.054

Cited by 3 publications

(2 citation statements)

References 97 publications

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“…In addition to the standard variations mentioned above, researchers have explored various enhancement techniques to refine MRFO. Mahata et al (2024) introduced a novel process within the algorithmic framework of MRFO, aiming to enhance the algorithm's stability through a particle reinitialization procedure, fundamentally modifying the position updating mechanism during the foraging phase. Abd utilized fractional-order (FO) calculus during the exploitation phase, enhancing the local search capability of the conventional MRFO approach and mitigating the risk of getting stuck at local optima.…”

Section: Other Variantsmentioning

confidence: 99%

“…The proposed OL-MRFO-VC algorithm, combined with Kapur entropy, effectively identified optimal threshold configurations in RGB components of images, demonstrating improved accuracy and robustness in image segmentation tasks, particularly at higher threshold levels. Mahata et al (2024) presented an MRFO algorithm for adaptive infinite impulse response (IIR) system identification, ensuring stability by incorporating a pole-finding routine.…”

Section: Engineering Problemsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Manta Ray Foraging Optimization Algorithm of Variants and Applications

Zheng,

Wang,

et al. 2024

International Journal of Swarm Intelligence Research

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This review paper delves into the original MRFO and its variants, focusing on single-objective algorithms including but not limited to hybrid algorithms, learning strategies, multiple populations and dynamic parameter adjustment, highlighting the improvements made to enhance the algorithm's efficiency in global optimization, accelerate convergence rates, and improve its capacity to evade local optima. MRFO has emerged as an effective tool for solving complex optimization problems across various domains, including energy optimization, biomedical field, engineering problems, and others. A comprehensive analysis of applications of MRFO in different fields is provided, emphasizing its adaptability and efficacy. The paper concludes with a discussion on the challenges faced by MRFO and potential future research directions, aiming to consolidate the current research status and guide future investigations.

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Section: Other Variantsmentioning

confidence: 99%

Section: Engineering Problemsmentioning

confidence: 99%

A Survey on Manta Ray Foraging Optimization Algorithm of Variants and Applications

Zheng,

Wang,

et al. 2024

International Journal of Swarm Intelligence Research

View full text Add to dashboard Cite

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Automated Design of CMOS-DACML Circuit

Mallick,

Mahata,

Layek

et al. 2024

2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS)

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An Enhanced Symmetric Sand Cat Swarm Optimization with Multiple Strategies for Adaptive Infinite Impulse Response System Identification

Du,

Zhang,

Fang

2024

Symmetry

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An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for system modeling remains complicated. The swarm intelligence algorithms facilitate the IIR filter’s parameters by exploring parameter domains and exploiting acceptable filter sets. This paper presents an enhanced symmetric sand cat swarm optimization with multiple strategies (MSSCSO) to achieve adaptive IIR system identification. The principal objective is to recognize the most appropriate regulating coefficients and to minimize the mean square error (MSE) between an unidentified system’s input and the IIR filter’s output. The MSSCSO with symmetric cooperative swarms integrates the ranking-based mutation operator, elite opposition-based learning strategy, and simplex method to capture supplementary advantages, disrupt regional extreme solutions, and identify the finest potential solutions. The MSSCSO not only receives extensive exploration and exploitation to refrain from precocious convergence and foster computational efficiency; it also endures robustness and reliability to facilitate demographic variability and elevate estimation precision. The experimental results manifest that the practicality and feasibility of the MSSCSO are superior to those of other methods in terms of convergence speed, calculation precision, detection efficiency, regulating coefficients, and MSE fitness value.

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Reduced order infinite impulse response system identification using manta ray foraging optimization

Cited by 3 publications

References 97 publications

A Survey on Manta Ray Foraging Optimization Algorithm of Variants and Applications

A Survey on Manta Ray Foraging Optimization Algorithm of Variants and Applications

Automated Design of CMOS-DACML Circuit

An Enhanced Symmetric Sand Cat Swarm Optimization with Multiple Strategies for Adaptive Infinite Impulse Response System Identification

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