Aiming at the problem of optimal siting of charging station for electric taxis, a siting model of charging station for electric taxis was established with the unmet charging demand and the minimum fixed cost of constructing new charging station as objective functions, and a model solution method based on improved multi-objective particle swarm optimization was proposed. In order to solve the performance bottleneck of computing unmet charging demand, a Graphics Processing Unit (GPU)-based unmet charging demand parallel algorithm was designed. Experimental results demonstrat that the running time of the parallel algorithm is about 10%-12% of that of CPU-based serial algorithm. Beijing was taken as an example of applying the proposed charging station siting model, and related multi-source data was collected and processed. The results show that the proposed optimal siting scheme for charging station is feasible.

When Sunday algorithm is applied into the Chinese version of Unicode, there are some problems. On one hand, it causes the expansion of space if using Chinese characters directly to generate a failed jump table. On the other hand, it can reduce the space consumption at the cost of matching speed when Chinese characters are split into two bytes. Concerning the degradation of time performance produced by applying Sunday algorithm to the character-splitting environment of Chinese version of Unicode, in combination with internal relevance of Chinese unit in Unicode, the improved algorithm in this paper optimized the auxiliary jump table and matching rules in original Sunday algorithm in the character-splitting environment. Consequently, the proposed algorithm not only solves the problem of space expansion, but also improves time performance of Sunday algorithm in this environment. Finally, the improved time and space performance of the algorithm gets proved via simulation.