Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge
Joint Authors
Pu, Xun
Chen, ShanXiong
Yu, XianPing
Zhang, Le
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-01
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
A Multilayer Perceptron (MLP) is a feedforward neural network model consisting of one or more hidden layers between the input and output layers.
MLPs have been successfully applied to solve a wide range of problems in the fields of neuroscience, computational linguistics, and parallel distributed processing.
While MLPs are highly successful in solving problems which are not linearly separable, two of the biggest challenges in their development and application are the local-minima problem and the problem of slow convergence under big data challenge.
In order to tackle these problems, this study proposes a Hybrid Chaotic Biogeography-Based Optimization (HCBBO) algorithm for training MLPs for big data analysis and processing.
Four benchmark datasets are employed to investigate the effectiveness of HCBBO in training MLPs.
The accuracy of the results and the convergence of HCBBO are compared to three well-known heuristic algorithms: (a) Biogeography-Based Optimization (BBO), (b) Particle Swarm Optimization (PSO), and (c) Genetic Algorithms (GA).
The experimental results show that training MLPs by using HCBBO is better than the other three heuristic learning approaches for big data processing.
American Psychological Association (APA)
Pu, Xun& Chen, ShanXiong& Yu, XianPing& Zhang, Le. 2018. Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge. Scientific Programming،Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1214671
Modern Language Association (MLA)
Pu, Xun…[et al.]. Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge. Scientific Programming No. 2018 (2018), pp.1-7.
https://search.emarefa.net/detail/BIM-1214671
American Medical Association (AMA)
Pu, Xun& Chen, ShanXiong& Yu, XianPing& Zhang, Le. Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge. Scientific Programming. 2018. Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1214671
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214671