Grey wolf optimizer-based back-propagation neural network algorithm

Hassanin, Mohamed F.; Shoeb, Abdullah M.; Hassanien, Aboul Ella

doi:10.1109/icenco.2016.7856471

Cited by 18 publications

(13 citation statements)

References 10 publications

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“…where − → D is the distance between the individual and the prey, t is the iteration index, − → X p is the position of the prey, − → X is the position of the individual, and − → A and − → C are coefficients respectively defined as follows [24]: where − → r 1 and − → r 2 are random number between [0, 1] and − → a decreases linearly from 2 to 0 through each iteration. The position of an individual is updated based on the positions of α, β, and δ as follows:…”

Section: Gray Wolf Optimizermentioning

confidence: 99%

Indoor Positioning System Using Artificial Neural Network With Swarm Intelligence

2020

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The global positioning system is the most popular outdoor positioning system; however, it is inapplicable to indoor environments. In this study, we created a hybrid algorithm, which applies an artificial neural network to resolve the problems of indoor positioning and a swarm-intelligence algorithm to perform the time-consuming task of adjusting parameters. Experiment results demonstrate that the positioning accuracy of the proposed hybrid algorithm is equivalent to the exhaustive method with search times that are far shorter.

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Section: Gray Wolf Optimizermentioning

confidence: 99%

Indoor Positioning System Using Artificial Neural Network With Swarm Intelligence

2020

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“…Generally, GWO [33] considered as the new metaheuristics swarm intelligence algorithm. Moreover, GWO is extensively modified for a broad range of optimization issues because of its imposing characteristics over other swarm intelligence approaches.…”

Section: Conventional Optimization Gwo Algorithmmentioning

confidence: 99%

Localization and Sizing of UPFC: A Hybrid Optimization Algorithm for Solving Reactive Power Dispatch

Kapse¹,

Daigavane²,

Daigavane³

2019

JCMPS

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In this paper, a method to determine the multi-objective ORPD issue in an unbalanced bus system is presented. In contrast to conventional ORPD issue, the proposed method attains the improvement in voltage enhancement, maximization in loadability, enhancement in ATC, minimization of loss under the unbalanced criteria. In order to determine the optimal positioning and sizing of the UPFC, the ORPD is accomplished by utilizing a hybrid CSA and GWO algorithm. Finally, the simulation outcomes on the benchmark IEEE test bus systems under unbalanced conditions reveal the ORPD performance of the proposed method against the conventional methods. Even under the overloading circumstances, the proposed method has shown its performance in retaining an optimal trade-off between the multiple objectives. Here, the analysis also comprises an examination of the sensitivity of the method over the overloading conditions of the bus system. At last, the simulation outcomes assure that the proposed method is better than the traditional methods such as GWO and CSA.

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“…Hybridization of backpropagation with soft computing techniques can eliminate such problems and can give a better solution [14]. Some of the optimization techniques available in literature for weights optimization are the GA [6], Particle Swarm Optimization (PSO) [7], Hybrid GA and PSO [11], Cuckoo Search [9], Grey Wolf Optimizer (GWO) [8] and whale optimization [10].…”

Section: Introductionmentioning

confidence: 99%

Medical Image Compression using Neural Network with HGAPSO Optimization

H.K.*,

2019

IJITEE

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Lossy medical image compression has become increasingly attractive due to a drastic increase in the number of images used for diagnosis and treatment. The work focused on developing a feed-forward neural network for compression of medical images with optimization of weights using hybrid genetic and particle swarm (HGAPSO) optimization technique. The neural network can achieve a better compressed & decompressed image only with the proper training and optimized weights. Training algorithms such as back-propagation algorithm (BPA) traps to local minima rather than the global one which degrades the quality of a reconstructed image. In this work, HGAPSO optimization is adopted to overcome the drawback of BPA. HGAPSO parameters are carefully chosen to have better exploitation & exploration in the search area, which avoids the algorithm from trapping to the local minima. High-quality results of Genetic Algorithm (GA) obtained using selection, crossover and mutation can provide quality guidance for PSO which improves the results of the proposed system. The performance of the proposed work is evaluated on a raw medical image database based on PSNR, MSE, and CR. The experiment is simulated for 16-4-16 neural network architecture and a compression ratio of 75 % is achieved. The results obtained indicated that with proper training PSNR could be improved by 1.98 %.

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Grey wolf optimizer-based back-propagation neural network algorithm

Cited by 18 publications

References 10 publications

Indoor Positioning System Using Artificial Neural Network With Swarm Intelligence

Indoor Positioning System Using Artificial Neural Network With Swarm Intelligence

Localization and Sizing of UPFC: A Hybrid Optimization Algorithm for Solving Reactive Power Dispatch

Medical Image Compression using Neural Network with HGAPSO Optimization

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