For time series forecasting, the problem that we often
encounter is how to increase the prediction accuracy as much as
possible with the irregular and noise data. This study proposes a
novel multilayer feedforward neural network based on the improved
particle swarm optimization with adaptive genetic operator (IPSO-
MLFN). In the proposed IPSO, inertia weight is dynamically
adjusted according to the feedback from particles' best memories,
and acceleration coefficients are controlled by a declining arccosine and an increasing arccosine function.
Further, a crossover rate which only depends on generation and does not associate with the individual fitness is designed.
Finally, the parameters of MLFN are optimized by IPSO. The empirical results on the container throughput forecast of
Shenzhen Port show that forecasts with IPSO-MLFN model are more conservative and credible.