LAPSE:2021.0586
Published Article
LAPSE:2021.0586
Extreme Learning Machine Based on Firefly Adaptive Flower Pollination Algorithm Optimization
Ting Liu, Qinwei Fan, Qian Kang, Lei Niu
June 29, 2021
Extreme learning machine (ELM) has aroused a lot of concern and discussion for its fast training speed and good generalization performance, and it has been used diffusely in both regression and classification problems. However, on account of the randomness of input parameters, it requires more hidden nodes to obtain the desired accuracy. In this paper, we come up with a firefly-based adaptive flower pollination algorithm (FA-FPA) to optimize the input weights and thresholds of the ELM algorithm. Nonlinear function fitting, iris classification and personal credit rating experiments show that the ELM with FA-FPA (FA-FPA-ELM) can obtain significantly better generalization performance (such as root mean square error, classification accuracy) than traditional ELM, ELM with firefly algorithm (FA-ELM), ELM with flower pollination algorithm (FPA-ELM), ELM with genetic algorithm (GA-ELM) and ELM with particle swarm optimization (PSO-ELM) algorithms.
Keywords
extreme learning machine, firefly algorithm, flower pollination algorithm, Optimization
Suggested Citation
Liu T, Fan Q, Kang Q, Niu L. Extreme Learning Machine Based on Firefly Adaptive Flower Pollination Algorithm Optimization. (2021). LAPSE:2021.0586
Author Affiliations
Liu T: School of Science, Xi’an Polytechnic University, Xi’an 710048, China
Fan Q: School of Science, Xi’an Polytechnic University, Xi’an 710048, China; School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China [ORCID]
Kang Q: School of Science, Xi’an Polytechnic University, Xi’an 710048, China
Niu L: School of Science, Xi’an Polytechnic University, Xi’an 710048, China
Journal Name
Processes
Volume
8
Issue
12
Article Number
E1583
Year
2020
Publication Date
2020-12-01
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8121583, Publication Type: Journal Article
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LAPSE:2021.0586
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doi:10.3390/pr8121583
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Jun 29, 2021
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[v1] (Original Submission)
Jun 29, 2021
 
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Original Submitter
Calvin Tsay
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