Vessel traffic flow forecasting by RSVR with chaotic cloud simulated annealing genetic algorithm and KPCA
The prediction of vessel traffic flow is complicated, its accuracy is influenced by uncertain socio-economic factors, especially by the singular points existed in the statistical data. Recently, the robust v-support vector regression model (RSVR) has been successfully employed to solve non-linear regression and time-series problems with the singular points. This paper will firstly propose a novel hybrid algorithm, namely chaotic cloud simulated annealing genetic algorithm (CcatCSAGA) for optimizing the parameters of RSVR, to improve the performance of vessel traffic flow prediction. In which, the proposed CcatCSAGA employs cat mapping to carefully expand variable searching space, to overcome premature local optimum, and uses cloud model efficiently to search a better solution in a small neighborhood of the current optimal solution, to improve the search efficiency. Secondly, the kernel principal component analysis (KPCA) algorithm is adopted to determine the final input vectors from the candidate input variables. Finally, a numerical example of vessel traffic flow and its influence factors data from Tianjin are employed to test the forecasting performance of the proposed KRSVR-CcatCSAGA model.
vessel traffic flow forecasting cloud model chaotic mapping genetic algorithm (ga) simulated annealing (sa) support vector regression (svr)