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

Nebojsa Bacanin, Ruxandra Stoean, Miodrag Zivkovic, Aleksandar Petrovic, Tarik A. Rashid and Timea Bezdan
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Nebojsa Bacanin: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
Ruxandra Stoean: Romanian Institute of Science and Technology, Str. Virgil Fulicea 3, 400022 Cluj-Napoca, Romania
Miodrag Zivkovic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
Aleksandar Petrovic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
Tarik A. Rashid: Computer Science and Engineering, University of Kurdistan Hewler, 30 Meter Avenue, Erbil 44001, Iraq
Timea Bezdan: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia

Mathematics, 2021, vol. 9, issue 21, 1-33

Abstract: 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.

Keywords: convolutional neural networks; dropout; regularization; metaheuristics; swarm intelligence; optimization; firefly algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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