An effective co-evolutionary algorithm based on artificial bee colony and differential evolution for time series predicting optimization
Yun Yang, Zongtao Duan
Abstract
Abstract Non-linear model optimization for predicting time series is a challenge problem. In Intelligent Transportation Systems (ITS) application, the indispensable short-term traffic flow prediction with big data makes the problem worst. To improve the prediction accuracy and ensure real-time performance in the big data environment, we propose a novel co-evolutionary artificial bee colony (ABC) improved by differential evolution (DE) optimization algorithm combined with a traffic flow predicting model trained by extreme learning machine (ELM) neural network. The proposed model can inherit the better generalization performance and the less training time consumption of the standard ELM, and can achieve a more balanced search strategy with the optimized weights and biases to overcome the random initialization deficiency of the typical ELM, and successfully obtain higher prediction accuracy compared with state-of-the-art methods. To verify the efficiency of the proposed model, we apply it to Lozi and Tent chaotic time series simulations and measured traffic flow time series experiments. Simulation and experimental results demonstrate that the proposed model has superior performance and competitive computational efficiency.