Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification

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

doi:10.3390/math10203821

Cited by 34 publications

(14 citation statements)

References 56 publications

(66 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chaos is a phenomenon that can exhibit non-linear changes in future behavior when its initial condition is even slightly altered. Additionally, it is described as a semi-random behavior generated by nonlinear deterministic systems [25]. One of main search algorithms is Chaos Optimization Algorithm (COA) which moves variables and parameters from the chaos to the solution space.…”

Section: Chaotic Mapsmentioning

confidence: 99%

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for Feature Selection

Abdelrazek,

Elaziz,

El-Baz

2023

Preprint

View full text Add to dashboard Cite

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which select optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and three other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the DMO, PSO, WOA and ACO methods used in feature selection.

show abstract

Section: Chaotic Mapsmentioning

confidence: 99%

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for Feature Selection

Abdelrazek,

Elaziz,

El-Baz

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The main purpose of MTL is to share knowledge across multiple similar tasks in order to improve the performance of each individual task. A related concept is the swarm intelligence optimization algorithm, 36,37 which is often utilized for system identification. This algorithm simulates various group behaviors of creatures found in nature and utilizes mutual communication and cooperation among individuals in the group to achieve optimal parameter search and identification.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed online multi‐task sparse identification for multiple systems with asynchronous updates

Zhang,

Luan,

Ding

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Simultaneous online identification of the structure and parameters for multiple systems can use the multi‐task learning to share parameter sparse patterns across different identification tasks to improve the estimation accuracy. To reduce the communication and computational burden, improve the convergence rate and increase the reliability of the identification task, a distributed asynchronous multi‐task sparse identification algorithm is proposed. The alternating minimization algorithm is applied to design a distributed multi‐task loss function. Then by constructing the auxiliary matrix and transforming the sparse constraint on the parameter matrix to the constructed auxiliary matrix, the asynchronous parameter update and sparse model update can be realized. The algorithm uses a recursive form to complete online identification which is feasible to analyze. After proving the convergence of the identification algorithm, we present an example to support the theoretical analysis and show the advantages of the proposed algorithm.

show abstract

“…In [ 84 ], a recursive least squares, decomposition least squares, and interval-varying least squares were used for ARX identification. In [ 85 ], dwarf mongoose optimization is used for system identification of the ARX model. In [ 86 ], multi-innovation fractional least mean squares were used in estimation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification

Cited by 34 publications

References 56 publications

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for Feature Selection

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for Feature Selection

Distributed online multi‐task sparse identification for multiple systems with asynchronous updates

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

Contact Info

Product

Resources

About