Extreme Learning Machine Optimized by Improved Firefly Algorithm

Abstract

As a simple and effective feedforward neural network, extreme learning machine (ELM) can randomly generate the connection weight between input layer and hidden layer and the hidden layer neuron threshold. Extreme learning machine can be used to solve the classification problem, but its classification accuracy is not good enough. In this paper, we proposed an improved firefly algorithm called IFA and use it to select the parameters in ELM. Experimental results showed that the IFA can solve the premature problem and the classification ability of ELM can be improved by the use of IFA.