A study on muti-strategy predator algorithm for passenger traffic prediction with big data

Abstract

Abstract In this paper, we study the big data multi-strategy predator algorithm for tourist flow prediction and explore the application of the algorithm in optimizing the tourist flow prediction model to improve the prediction accuracy and efficiency. An adversarial learning strategy extends the search space, an adaptive weighting factor balances the global and local search ability, and a variance operation combined with differential evolution is used to avoid local optimal traps. The experiment adopts variables such as network booking volume and search index as inputs for passenger flow prediction. The predator algorithm is trained by Extreme Learning Machine (ELM) to optimize the input weights and biases to build the FMMPAELM model. The results show that on the training samples, the FMMPA-ELM model predictions are highly consistent with the actual values, with a maximum prediction index of 200.On the test samples, although there are errors, the FMMPA-ELM model exhibits better prediction ability than the traditional ELM model. It is concluded that the FMMPAELM model can effectively improve the accuracy of passenger flow prediction and provide powerful decision support for the tourism industry.