The basic Artificial Bee Colony (ABC) algorithm has some shortcomings such as slow convergence, low precision and easily getting trapped in local optimum. To overcome these issues, an improved ABC algorithm with enhanced exploitation ability was proposed. On one hand, the obtained optimum solution was directly introduced into the search equations of employed bees in two different ways and guided the employed bees to perform neighborhood search, which enhanced the exploitation or local search ability of the algorithm. On the other hand, the search was performed by the combination of the current solution and its random neighborhood in the search equations of onlooker bees, which improved the global optimization ability of the algorithm. The simulation results on some common benchmark functions show that in convergence rate, precision, and global optimization or exploration ability, the proposed ABC algorithm is generally better than the other similar improved ABC algorithms such as global best ABC (ABC/best) algorithm, and some ABC algorithms with hybrid search strategy such as ABC algorithm with Variable Search Strategy (ABCVSS) and Multi-Search Strategy Cooperative Evolutionary (ABCMSSCE).

Similar string search algorithms based on Trie tree need to compute active-node set of a node by editing distance threshold. A large number of redundant computation leads to a high time and space complexity. A new algorithm named New-Trie-Stack was proposed, which utilized the symmetrical properties of active-node set and the dynamic programming method to improve the performance. It could avoid the redundancy cost on active-node set computing and storing; moreover, active-node sets were pruned. The experimental results show that New-Trie-Stack algorithm has lower time complexity and space complexity.

Precipitation data quality of regional automatic weather stations determines the missed alarm and false probability of weather warning, but it is difficult to verify the validity of the data by only relying on space and time correlation of automatic data. Precipitation of weather radar can verify automatic weather station data. However, it is difficult to determine the coverage of weather stations by radar station due to heterogeneous and complex geographical environment of radar station, which results in constant error-checking. This paper built a coverage matching algorithm of radar stations and automatic weather stations by using the layer volume scan mode and the radar beam occlusion model of weather radar. Automatic stations and radar stations matching software based on the algorithm can effectively support Automatic Weather Station (AWS) precipitation data inspection.