Dynamic social behavior algorithm for real-parameter optimization problems and optimization of hyper beamforming of linear antenna arrays

Sankar, K. Prajindra; Kiong, Tiong Sieh; Paw, Johnny Koh Siaw

doi:10.1016/j.engappai.2017.06.027

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…where � ⃗ a linearly decreases from 2 to 0, ⃗ r 1 and ⃗ r 2 are random vector generated within 0 and 1. Detailed mathematical illustration of the concept of prey encircling can be found in the work of Mirjalili et al [41] and Prajindra et al [59].…”

Section: Grey Wolf Optimisation (Gwo) Algorithmmentioning

confidence: 99%

“…However, considering the theory of No Free Lunch [58] which implies that no single optimiser can boast of being superior to the others for all optimisation tasks, and as such meta-heuristics are task-specific, inspires the selection of GWO as the choice for training the MLP classifier in this study. GWO is a recently proposed Swarmbased algorithm which emulates the social leadership hierarchy and unique hunting mechanism of Grey Wolfs [59]. When compared to other metaheuristics, the unique search mechanism of the GWO significantly enhances the algorithm's ability to optimally achieve a balance between the exploration and exploitation search phases.…”

Section: Introductionmentioning

confidence: 99%

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A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

et al. 2021

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Considering the complexities and challenges in the classification of multiclass and imbalanced fault conditions, this study explores the systematic combination of unsupervised and supervised learning by hybridising clustering (CLUST) and optimised multi-layer perceptron neural network with grey wolf algorithm (GWO-MLP). The hybrid technique was meticulously examined on a historical hydraulic system dataset by first, extracting and selecting the most significant statistical time-domain features. The selected features were then grouped into distinct clusters allowing for reduced computational complexity through a comparative study of four different and frequently used categories of unsupervised clustering algorithms in fault classification. The Synthetic Minority Over Sampling Technique (SMOTE) was then employed to balance the classes of the training samples from the various clusters which then served as inputs for training the supervised GWO-MLP. To validate the proposed hybrid technique (CLUST-SMOTE-GWO-MLP), it was compared with its distinct modifications (variants). The superiority of CLUST-SMOTE-GWO-MLP is demonstrated by outperforming all the distinct modifications in terms of test accuracy and seven other statistical performance evaluation metrics (error rate, sensitivity, specificity, precision, F score, Mathews Correlation Coefficient and geometric mean). The overall analysis indicates that the proposed CLUST-SMOTE-GWO-MLP is efficient and can be used to classify multiclass and imbalanced fault conditions. Article Highlights The issue of multiclass and imbalanced class outputs is addressed for improving predictive maintenance. A multiclass fault classifier based on clustering and optimised multi-layer perceptron with grey wolf is proposed. The robustness and feasibility of the proposed technique is validated on a complex hydraulic system dataset.

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Section: Grey Wolf Optimisation (Gwo) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

et al. 2021

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“…Given the topological complexity, conventional enhancement methods involving equivalent network models or experience-driven parametric studies can only yield sub-optimal designs, and are generally unsuitable for handling multiple design goals, conditions on electrical performance figures, and multiple parameters. Instead, rigorous numerical optimization is recommended 24 , 25 . Probably the most serious bottleneck thereof constitutes the inflated computational cost that is problematic even for local parameter adjustment (e.g., gradient-based 26 ).…”

Section: Introductionmentioning

confidence: 99%

On nature-inspired design optimization of antenna structures using variable-resolution EM models

Kozieł

Pietrenko‐Dabrowska

2023

Sci Rep

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Numerical optimization has been ubiquitous in antenna design for over a decade or so. It is indispensable in handling of multiple geometry/material parameters, performance goals, and constraints. It is also challenging as it incurs significant CPU expenses, especially when the underlying computational model involves full-wave electromagnetic (EM) analysis. In most practical cases, the latter is imperative to ensure evaluation reliability. The numerical challenges are even more pronounced when global search is required, which is most often carried out using nature-inspired algorithms. Population-based procedures are known for their ability to escape from local optima, yet their computational efficiency is poor, which makes them impractical when applied directly to EM models. A common workaround is the utilization of surrogate modeling techniques, typically in the form of iterative prediction-correction schemes, where the accumulated EM simulation data is used to identify the promising regions of the parameter space and to refine the surrogate model predictive power at the same time. Notwithstanding, implementation of surrogate-assisted procedures is often intricate, whereas their efficacy may be hampered by the dimensionality issues and considerable nonlinearity of antenna characteristics. This work investigates the benefits of incorporating variable-resolution EM simulation models into nature-inspired algorithms for optimization of antenna structures, where the model resolution pertains to the level of discretization density of an antenna structure in the full-wave simulation model. The considered framework utilizes EM simulation models which share the same physical background and are selected from a continuous spectrum of allowable resolutions. The early stages of the search process are carried out with the use of the lowest fidelity model, which is subsequently automatically increased to finally reach the high-fidelity antenna representation (i.e., considered as sufficiently accurate for design purposes). Numerical validation is executed using several antenna structures of distinct types of characteristics, and a particle swarm optimizer as the optimization engine. The results demonstrate that appropriate resolution adjustment profiles permit considerable computational savings (reaching up to eighty percent in comparison to high-fidelity-based optimization) without noticeable degradation of the search process reliability. The most appealing features of the presented approach—apart from its computational efficiency—are straightforward implementation and versatility.

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A Survey on Hybrid, 3D, Interference Mitigation and Secure Data Beamforming Techniques for 5G System

Yadav

Tripathi

2020

Wireless Pers Commun

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Dynamic social behavior algorithm for real-parameter optimization problems and optimization of hyper beamforming of linear antenna arrays

Cited by 4 publications

References 33 publications

A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

On nature-inspired design optimization of antenna structures using variable-resolution EM models

A Survey on Hybrid, 3D, Interference Mitigation and Secure Data Beamforming Techniques for 5G System

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