A firefly algorithm for modular granular neural networks optimization applied to iris recognition

Sánchez, Daniela; Melín, Patricia; Carpio, Juan Martín; Soberanes, Héctor José Puga

doi:10.1109/ijcnn.2016.7727191

Cited by 5 publications

(13 citation statements)

References 18 publications

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“…The proposed method uses modular granular neural networks, this kind of artificial neural network was proposed in [ 7 ] and [ 37 ], and their optimization were performed using, respectively, a hierarchical genetic algorithm and a firefly algorithm. In this work, the optimization is performed using a grey wolf optimizer and a comparison among HGA, FA, and GWO is performed to know which of these techniques is better for MGNN optimization.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…One of the most important parameters of the architecture is its learning algorithm, backpropagation algorithms are used in the training phase to perform the learning, and 3 variations of this algorithm can be selected by the proposed optimizer: gradient descent with scaled conjugate gradient (SCG), gradient descent with adaptive learning and momentum (GDX), and gradient descent with adaptive learning (GDA). These 3 algorithms were selected because they have between demonstrated to be the fastest algorithms and with them better performances and results have been obtained [ 6 , 7 , 37 – 39 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The main comparisons with the proposed method are the optimizations proposed in [ 7 , 37 , 38 ]. In the first one a hierarchical genetic algorithm is developed, in the second and third work a firefly algorithm is developed to perform the MGNN optimization, and to have a fair comparison the number of individuals/fireflies and number of generations/iterations used in [ 7 , 37 , 38 ] are the same used by the proposed method in this work; obviously for the GWO these values are number of search agents and iterations. In Table 1 , the values of the parameters used for each optimization algorithm are presented.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…On the other hand, granular computing defines granules as classes or subsets used for complex applications to build computational models where a large amounts of data and information are used [ 19 , 41 , 42 ]. In this work granular computing is applied to perform granulation of information into subsets that also define number of modules of a modular neural network; the combination of modular neural networks and granular computing was already proposed in [ 7 , 37 , 38 ], where the advantages of modular granular neural networks over conventional neural networks and modular neural networks were widely demonstrated. In [ 7 ], the modular granular neural network architectures were designed using an improvement of a genetic algorithm, a hierarchical genetic algorithm (HGA), where the main differences between them are the control genes in the HGA that allow activating and deactivating genes allowing solving complex problems.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 38 ], a firefly algorithm was proposed for MGNN optimization using an experts submodules for each division of image. In [ 37 ], also modular granular neural network architectures were designed but using a firefly algorithm and without an expert submodule for each division of image. In this work, the design of MGNN architecture is performed and applied to human recognition based on ear, face, and iris, but using a grey wolf optimizer, statistical comparisons are performed to define which of these optimization techniques is better to perform optimization of MGNNs.…”

Section: Introductionmentioning

confidence: 99%

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A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

Sánchez

Melín

Castillo

2017

Computational Intelligence and Neuroscience

Self Cite

104

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A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

Sánchez

Melín

Castillo

2017

Computational Intelligence and Neuroscience

Self Cite

104

View full text Add to dashboard Cite

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Improved Firefly Algorithm Based on Genetic Algorithm Operators for Energy Efficiency in Smart Buildings

2019

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Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

Nayak

Naik

Dinesh³

et al. 2020

SN COMPUT. SCI.

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Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA.

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A firefly algorithm for modular granular neural networks optimization applied to iris recognition

Cited by 5 publications

References 18 publications

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

Improved Firefly Algorithm Based on Genetic Algorithm Operators for Energy Efficiency in Smart Buildings

Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

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