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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Notes
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.
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Acknowledgments
This paper is sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, National Key Technology R&D Program in 12th Five-year Plan of China (No. 2013BAI13B06).
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Yang, Z., Zhang, T. & Zhang, D. A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training. Cogn Neurodyn 10, 73–83 (2016). https://doi.org/10.1007/s11571-015-9358-9
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DOI: https://doi.org/10.1007/s11571-015-9358-9