In the standard firefly algorithm, each firefly has the same step settings and its values decrease from iteration to iteration. Therefore, it may fall into the local optimum. Furthermore, the decreasing of step is restrained by the maximum of iteration, which has an influence on the convergence speed and precision. In order to avoid falling into the local optimum and reduce the impact of the maximum of iteration, a self-adaptive step firefly algorithm is proposed in the paper. Its core idea is setting the step of each firefly varying with the iteration, according to each firefly’s historical information and current situation. Experiments are made to show the performance of our approach compared with the standard FA, based on sixteen standard testing benchmark functions. The results reveal that our method can prevent the premature convergence and improve the convergence speed and accurateness.

With the rapid development of ocean transportation, vessel trajectory mining and analysis have become more and more important. Trajectory clustering is the basis of practical applications. Traditional trajectory similarity calculation methods are relatively low in accuracy and efficiency, the existing deep learning methods mostly extract features of static ones, ignoring the multi-feature combination of dynamic and static features. This paper proposes a static-dynamic-feature fusion method based on convolutional auto-encoder, which can extract more perfect trajectory features in artificial intelligence field. Experiments on real datasets have demonstrated that compared with other methods, there is at least a 5%-10% improvement in terms of classic clustering metrics and lower time consumption.