Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization

Swarm intelligence techniques have been created to respond to theoretical and practical global optimization problems. This paper puts forward an enhanced version of the firefly algorithm that corrects the acknowledged drawbacks of the original method, by an explicit exploration mechanism and a chaot...

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Autores principales:	Nebojsa Bacanin, Ruxandra Stoean, Miodrag Zivkovic, Aleksandar Petrovic, Tarik A. Rashid, Timea Bezdan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	convolutional neural networks dropout regularization metaheuristics swarm intelligence optimization Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/052c5d230f964fffa39742d8afb6a1f7
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id	oai:doaj.org-article:052c5d230f964fffa39742d8afb6a1f7
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spelling	oai:doaj.org-article:052c5d230f964fffa39742d8afb6a1f72021-11-11T18:15:55ZPerformance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization10.3390/math92127052227-7390https://doaj.org/article/052c5d230f964fffa39742d8afb6a1f72021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2705https://doaj.org/toc/2227-7390Swarm intelligence techniques have been created to respond to theoretical and practical global optimization problems. This paper puts forward an enhanced version of the firefly algorithm that corrects the acknowledged drawbacks of the original method, by an explicit exploration mechanism and a chaotic local search strategy. The resulting augmented approach was theoretically tested on two sets of bound-constrained benchmark functions from the CEC suites and practically validated for automatically selecting the optimal dropout rate for the regularization of deep neural networks. Despite their successful applications in a wide spectrum of different fields, one important problem that deep learning algorithms face is overfitting. The traditional way of preventing overfitting is to apply regularization; the first option in this sense is the choice of an adequate value for the dropout parameter. In order to demonstrate its ability in finding an optimal dropout rate, the boosted version of the firefly algorithm has been validated for the deep learning subfield of convolutional neural networks, with respect to five standard benchmark datasets for image processing: MNIST, Fashion-MNIST, Semeion, USPS and CIFAR-10. The performance of the proposed approach in both types of experiments was compared with other recent state-of-the-art methods. To prove that there are significant improvements in results, statistical tests were conducted. Based on the experimental data, it can be concluded that the proposed algorithm clearly outperforms other approaches.Nebojsa BacaninRuxandra StoeanMiodrag ZivkovicAleksandar PetrovicTarik A. RashidTimea BezdanMDPI AGarticleconvolutional neural networksdropoutregularizationmetaheuristicsswarm intelligenceoptimizationMathematicsQA1-939ENMathematics, Vol 9, Iss 2705, p 2705 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolutional neural networks dropout regularization metaheuristics swarm intelligence optimization Mathematics QA1-939
spellingShingle	convolutional neural networks dropout regularization metaheuristics swarm intelligence optimization Mathematics QA1-939 Nebojsa Bacanin Ruxandra Stoean Miodrag Zivkovic Aleksandar Petrovic Tarik A. Rashid Timea Bezdan Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization
description	Swarm intelligence techniques have been created to respond to theoretical and practical global optimization problems. This paper puts forward an enhanced version of the firefly algorithm that corrects the acknowledged drawbacks of the original method, by an explicit exploration mechanism and a chaotic local search strategy. The resulting augmented approach was theoretically tested on two sets of bound-constrained benchmark functions from the CEC suites and practically validated for automatically selecting the optimal dropout rate for the regularization of deep neural networks. Despite their successful applications in a wide spectrum of different fields, one important problem that deep learning algorithms face is overfitting. The traditional way of preventing overfitting is to apply regularization; the first option in this sense is the choice of an adequate value for the dropout parameter. In order to demonstrate its ability in finding an optimal dropout rate, the boosted version of the firefly algorithm has been validated for the deep learning subfield of convolutional neural networks, with respect to five standard benchmark datasets for image processing: MNIST, Fashion-MNIST, Semeion, USPS and CIFAR-10. The performance of the proposed approach in both types of experiments was compared with other recent state-of-the-art methods. To prove that there are significant improvements in results, statistical tests were conducted. Based on the experimental data, it can be concluded that the proposed algorithm clearly outperforms other approaches.
format	article
author	Nebojsa Bacanin Ruxandra Stoean Miodrag Zivkovic Aleksandar Petrovic Tarik A. Rashid Timea Bezdan
author_facet	Nebojsa Bacanin Ruxandra Stoean Miodrag Zivkovic Aleksandar Petrovic Tarik A. Rashid Timea Bezdan
author_sort	Nebojsa Bacanin
title	Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization
title_short	Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization
title_full	Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization
title_fullStr	Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization
title_full_unstemmed	Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization
title_sort	performance of a novel chaotic firefly algorithm with enhanced exploration for tackling global optimization problems: application for dropout regularization
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/052c5d230f964fffa39742d8afb6a1f7
work_keys_str_mv	AT nebojsabacanin performanceofanovelchaoticfireflyalgorithmwithenhancedexplorationfortacklingglobaloptimizationproblemsapplicationfordropoutregularization AT ruxandrastoean performanceofanovelchaoticfireflyalgorithmwithenhancedexplorationfortacklingglobaloptimizationproblemsapplicationfordropoutregularization AT miodragzivkovic performanceofanovelchaoticfireflyalgorithmwithenhancedexplorationfortacklingglobaloptimizationproblemsapplicationfordropoutregularization AT aleksandarpetrovic performanceofanovelchaoticfireflyalgorithmwithenhancedexplorationfortacklingglobaloptimizationproblemsapplicationfordropoutregularization AT tarikarashid performanceofanovelchaoticfireflyalgorithmwithenhancedexplorationfortacklingglobaloptimizationproblemsapplicationfordropoutregularization AT timeabezdan performanceofanovelchaoticfireflyalgorithmwithenhancedexplorationfortacklingglobaloptimizationproblemsapplicationfordropoutregularization
_version_	1718431863187439616

Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization

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