Robust genetically optimized skew laminates

Kalita, Kanak; Dey, Partha Pratim; Haldar, Salil

doi:10.1177/0954406218756943

Cited by 28 publications

(7 citation statements)

References 26 publications

(37 reference statements)

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“…Based on past research works on the same finite element formulation [30][31][32], it is revealed that an 18 × 18 mesh discretization of the composite plate using 9-node isoparametric elements is sufficiently accurate for the considered problems. To demonstrate the accuracy of the current FEM formulation, a validation example is included in Figure A1, Appendix A.…”

Section: Finite Element Methodsmentioning

confidence: 99%

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Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites

et al. 2021

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High-fidelity structural analysis using numerical techniques, such as finite element method (FEM), has become an essential step in design of laminated composite structures. Despite its high accuracy, the computational intensiveness of FEM is its serious drawback. Once trained properly, the metamodels developed with even a small training set of FEM data can be employed to replace the original FEM model. In this paper, an attempt is put forward to investigate the utility of radial basis function (RBF) metamodels in the predictive modelling of laminated composites. The effectiveness of various RBF basis functions is assessed. The role of problem dimensionality on the RBF metamodels is studied while considering a low-dimensional (2-variable) and a high-dimensional (16-variable) problem. The effect of uniformity of training sample points on the performance of RBF metamodels is also explored while considering three different sampling methods, i.e., random sampling, Latin hypercube sampling and Hammersley sampling. It is observed that relying only on the performance metrics, such as cross-validation error that essentially reuses training samples to assess the performance of the metamodels, may lead to ill-informed decisions. The performance of metamodels should also be assessed based on independent test data. It is further revealed that uniformity in training samples would lead towards better trained metamodels.

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Section: Finite Element Methodsmentioning

confidence: 99%

“…These three natural frequencies are treated here as the responses. The detailed formulation of the FEM used in this paper can be available in [30][31][32]. The FE formulation adopts a 9-node isoparametric element and is based on first-order shear deformation theory (FSDT).…”

Section: Problem 1: Low-dimensional (Ld) Problemmentioning

confidence: 99%

Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites

et al. 2021

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“…The objective is to identify and inhibit bot threats and improve the performance of botnet detection. The GA is a metaheuristic algorithm inspired by natural selection and evolution [31]. It involves using a population of candidate solutions (in this case, features) that undergo selection, crossover, and mutation operations to evolve towards an optimal solution.…”

Section: Genetic Algorithmmentioning

confidence: 99%

A Meta-Classification Model for Optimized ZBot Malware Prediction Using Learning Algorithms

et al. 2023

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Botnets pose a real threat to cybersecurity by facilitating criminal activities like malware distribution, attacks involving distributed denial of service, fraud, click fraud, phishing, and theft identification. The methods currently used for botnet detection are only appropriate for specific botnet commands and control protocols; they do not endorse botnet identification in early phases. Security guards have used honeypots successfully in several computer security defence systems. Honeypots are frequently utilised in botnet defence because they can draw botnet compromises, reveal spies in botnet membership, and deter attacker behaviour. Attackers who build and maintain botnets must devise ways to avoid honeypot traps. Machine learning methods support identification and inhibit bot threats to address the problems associated with botnet attacks. To choose the best features to feed as input to the machine learning classifiers to estimate the performance of botnet detection, a Kernel-based Ensemble Meta Classifier (KEMC) Strategy is suggested in this work. And particle swarm optimization (PSO) and genetic algorithm (GA) intelligent optimization algorithms are used to establish the ideal order. The model covered in this paper is employed to forecast Internet cyber security circumstances. The Binary Cross-Entropy (loss), the GA-PSO optimizer, the Softsign activation functions and ensembles were used in the experiment to produce the best results. The model succeeded because Forfileless malware, gathered from well-known datasets, achieved a total accuracy of 93.3% with a True Positive (TP) Range of 87.45% at zero False Positive (FP).

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“…Selecting the key parameters of GA, such as population size, number of generations, crossover rate and mutation rate, carefully is extremely important. 29,30 This algorithm regards all individuals in the population as research objects and achieves a stable, optimal breeding and selection process through the hybridisation and variation of individual genes, until the population puts forward the best solution. 31 Compared with traditional algorithms, GAs use global optimisation rather than point-to-point search, making it easier to achieve global optimality.…”

Section: Mathematical Model Of Ga-bpnnmentioning

confidence: 99%

Prediction of tool wear based on GA-BP neural network

Wei

Cong

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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The anisotropy and nonuniformity of wood-plastic composites (WPCs) affect the milling tool, which rapidly wears during high-speed milling of WPCs. Thus, the evolution mechanism of tool failure becomes complicated, and the prediction of tool wear cannot be precisely described mathematically. A neural network based on tool wear test was proposed to predict the tool wear condition during high-speed milling of WPCs. The traditional backpropagation (BP) neural network easily falls into the local optimal solution. A genetic algorithm (GA-BP) neural network prediction model was established by using the GA to optimise its initial weight and threshold. The BP model and the GA-BP model were evaluated in terms of mean square error and training times, and the generalisation verification was applied to the prediction model. After analysing and comparing the results of the two models, the GA-BP neural network model has better training speed and accuracy under the test conditions. The relative error between the predicted value and the actual value is controlled within 5%.

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Robust genetically optimized skew laminates

Cited by 28 publications

References 26 publications

Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites

Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites

A Meta-Classification Model for Optimized ZBot Malware Prediction Using Learning Algorithms

Prediction of tool wear based on GA-BP neural network

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