Using ant colony optimization to optimize long short-term memory recurrent neural networks

Abstract

This work examines the use of ant colony optimization (ACO) to improve long short-term memory (LSTM) recurrent neural networks (RNNs) by refining their cellular structure. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, which evolved 1000 different LSTM cell structures using 208 cores over 5 days. The new evolved LSTM cells showed an improvement in prediction accuracy of 1.37%, reducing the mean prediction error from 6.38% to 5.01% when predicting excessive engine vibrations 10 seconds in the future, while at the same time dramatically reducing the number of trainable weights from 21,170 to 11,650. The ACO optimized LSTM also performed significantly better than traditional Nonlinear Output Error (NOE), Nonlinear AutoRegression with eXogenous (NARX) inputs, and Nonlinear Box-Jenkins (NBJ) models, which only reached error rates of 11.45%, 8.47% and 9.77%, respectively. The ACO algorithm employed could be utilized to optimize LSTM RNNs for any time series data prediction task.