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Cognitive Neurodynamics

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01-02-2016 | Research Article

A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

Authors: Zhiyong Yang, Taohong Zhang, Dezheng Zhang

Published in: Cognitive Neurodynamics | Issue 1/2016

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Abstract

Extreme learning machine (ELM) is a novel and fast learning method to train single layer feed-forward networks. However due to the demand for larger number of hidden neurons, the prediction speed of ELM is not fast enough. An evolutionary based ELM with differential evolution (DE) has been proposed to reduce the prediction time of original ELM. But it may still get stuck at local optima. In this paper, a novel algorithm hybridizing DE and metaheuristic coral reef optimization (CRO), which is called differential evolution coral reef optimization (DECRO), is proposed to balance the explorative power and exploitive power to reach better performance. The thought and the implement of DECRO algorithm are discussed in this article with detail. DE, CRO and DECRO are applied to ELM training respectively. Experimental results show that DECRO-ELM can reduce the prediction time of original ELM, and obtain better performance for training ELM than both DE and CRO.

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To make \(F_b\) change dynamically, we set \(F_b = 0.9 -\frac{(0.9-0.4)t}{n_t}\), where t is the current iteration number, \(n_t\) is the total iteration number.

Atif M, Al-Sulaiman FA (2015) Optimization of heliostat field layout in solar central receiver systems on annual basis using differential evolution algorithm. Energy Convers Manag 95:1–9CrossRef

Belsley DA, Kuh E, Welsch RE (1980) Regression diagnostics: identifying influential data and sources of collinearity. Wiley, Hoboken, pp 244–261CrossRef

Bhadra T, Bandyopadhyay S (2015) Unsupervised feature selection using an improved version of differential evolution. Expert Syst Appl 42:4042–4053CrossRef

Birru HK, Chellapilla K, Rao SS (1999) Local search operators in fast evolutionary programming. In: Proceedings of the IEEE into Congress on Evolutionary Computation, pp 1506–1513

Chena Y, Mahalecb V, Chena Y, Liua X, Hea R, Suna K (2015) Reconfiguration of satellite orbit for cooperative observation using variable-size multi-objective differential evolution. Eur J Oper Res 242:10–20CrossRef

Chowdhury AR, Chetty M, Evans R (2015) Stochastic S-system modeling of gene regulatory network. Cogn Neurodyn 9:535–547

Dunnett CW (1955) A multiple comparison procedure for comparing several treatments with a control. J Am Stat Assoc 50:1096–1121CrossRef

Fanaee-T H, Gama J (2013) Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence. Springer, Berlin, pp 1–15

Garcła-Domingo B, Carmona CJ, Rivera-Rivas AJ, del Jesus MJ, Aguilera J (2015) A differential evolution proposal for estimating the maximum power delivered by CPV modules under real outdoor conditions. Expert Syst Appl 42:5452–5462CrossRef

Gerritsma J, Onnink R, Versluis A (1981) Geometry, resistance and stability of the delft systematic yacht hull series. Int Shipbuild Prog 28:276–297

Hamedia N, Iranshahib D, Rahimpoura MR, Raeissia S, Rajaeia H (2015) Development of a detailed reaction network for industrial upgrading of heavy reformates to xylenes using differential evolution technique. J Taiwan Inst Chem Eng 48:56–72CrossRef

Hayter AJ (1986) The maximum familywise error rate of fisher’s least significant difference test. J Am Stat Assoc 81:1000–1004CrossRef

http://archive.ics.uci.edu/ml/datasets/Yacht+Hydrodynamics

Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the international joint conference on neural networks (IJCNN2004), pp 25–29

Langdon WB, Poli R (2007) Evolving problems to learn about particle swarm optimizers and other search algorithms. IEEE Trans Evol Comput 11:561–578CrossRef

Lee S-Y, Song H-A, Amari S (2012) A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech. Cogn Neurodyn 6(6):525–535PubMedCentralCrossRefPubMed

Ortigosa I, Lopez R, Garcia J (2007) A neural networks approach to residuary resistance of sailing yachts prediction. In: Proceedings of the international conference on marine engineering MARINE

Quinlan R (1993) Combining instance-based and model-based learning. In: Proceedings on the tenth international conference of machine learning, pp 236–243

Ronkkonen J, Kukkonen S, Price KV (2005) Real parameter optimization with differential evolution. In: Proceedings of IEEE CEC, vol 1. pp 506–513

Roque CMC, Martins PALS (2015) Differential evolution optimization for the analysis of composite plates with radial basis collocation meshless method. Compos Struct 75:317–326CrossRef

Salcedo-Sanz S, Gallo-Marazuela D, Pastor-Snchez A, Carro-Calvo L, Portilla-Figueras A, Prieto L (2014) Offshore wind farm design with the coral reefs optimization algorithm. Renew Energy 63:109–115CrossRef

Salcedo-Sanz S, Pastor-Snchez A, Prieto L, Blanco-Aguilera A, Garcła-Herrera R (2014) Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization—extreme learning machine approach. Energy Convers Manag 87:10–18CrossRef

Salcedo-Sanz S, Casanova-Mateo C, Pastor-Snchez A, Snchez CGirn M (2014) Daily global solar radiation prediction based on a hybrid coral reefs optimization—extreme learning machine approach. Solar Energy 105:91–98CrossRef

Salcedo-Sanz S, Garcia-Diaz P, Portilla-Figueras JA, Del Ser J, Gil-Lopez S (2014) A coral reefs optimization algorithm for optimal mobile network deployment with electromagnetic pollution control criterion. Appl Soft Comput 24:239–248CrossRef

Salcedo-Sanz S, Pastor-Sanchez A, Del Ser J, Prieto L, Geem ZW (2015) A coral reefs optimization algorithm with harmony search operators for accurate wind speed prediction. Renew Energy 75:93–101CrossRef

Salcedo-Sanz S, Del Ser J, Landa-Torres I, Gil-Lpez S, Portilla-Figueras JA (2014) The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. Sci World J. Article ID 739768

Salcedo-Sanz S, Pastor-Snchez A, Gallo-Marazuela D, Portilla-Figueras A (2013) A novel coral reefs optimization algorithm for multi-objective problems. Lecture Notes in Computer Science, vol 8206. pp 326–333

Sarkara S, Dasb S, Chaudhuric SS (2015) A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution. Pattern Recognit Lett 54:27–35CrossRef

Storn R, Price K (1997) Differential evolutionła simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359CrossRef

Wang X, Lv Q, Wang B, Zhang L (2013) Airport detection in remote sensing images: a method based on saliency map. Cogn Neurodyn 7(2):143–154PubMedCentralCrossRefPubMed

Wennekers T, Palm G (2009) Syntactic sequencing in Hebbian cell assemblies. Cogn Neurodyn 3(4):429–441PubMedCentralCrossRefPubMed

Yeh IC (1988) Modeling of strength of high performance concrete using artificial neural networks. Cem Concr Res 28:1797–1808CrossRef

Zhu QY, Qin AK, Suganthan PN, Huang GB (2005) Evolutionary extreme learning machine. Pattern Recognit 38:1759–1763CrossRef

Title: A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training
Authors: Zhiyong Yang
Taohong Zhang
Dezheng Zhang
Publication date: 01-02-2016
Publisher: Springer Netherlands
Published in: Cognitive Neurodynamics / Issue 1/2016
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI: https://doi.org/10.1007/s11571-015-9358-9

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