In order to solve the problem of human aggressive behavior recognition, an aggressive behavior recognition method based on human joint points was proposed. Firstly, OpenPose was used to obtain the human joint point data of a single frame image, and nearest neighbor frame feature weighting method and piecewise polynomial regression were used to realize the completion of missing values caused by body self-occlusion and environmental factors. Then, the dynamic "safe distance" threshold was defined for each human body. If the true distance between the two people was less than the threshold, the behavior feature vector was constructed, including the human barycenter displacement between frames, the angular velocity of human joint rotation and the minimum attack distance during interaction. Finally, the improved LightGBM (Light Gradient Boosting Machine) algorithm, namely w-LightGBM (weight LightGBM), was used to realize the classification and recognition of aggressive behaviors. The public dataset UT-interaction was used to verify the proposed method, and the accuracy reached 95.45%. The results show that this method can effectively identify the aggressive behaviors from various angles.

Aiming at the problems of Artificial Bee Colony (ABC) algorithm, which are the single search mechanism and the high coupling between global search and local search, a Multi-Population ABC (MPABC) algorithm based on hybrid search was proposed. Firstly, the population was sorted according to the fitness value to get an ordered queue, which was divided into three sorted subgroups including random subgroup, core subgroup and balanced subgroup. Secondly, different difference vectors were constructed according to the corresponding individual selection mechanism and search strategy to different subgroups. Finally, in the process of group search, the effective control of individuals with different fitness functions was realized through three subgroups, thus improving the balance ability of global search and local search. The simulation results based on 16 benchmark functions show that compared with ABC algorithm with Variable Search Strategy (ABCVSS), Modified ABC algorithm based on selection probability (MABC), Particle Swarm-inspired Multi-Elitist ABC (PS-MEABC) algorithm, Multi-Search Strategy of the ABC (MSSABC) and Improved ABC algorithm for optimizing high-dimensional complex functions (IABC), MPABC achieves a better optimization effect; and on the solution of high dimensional (100 dimensions) problems, compared with ABC, MPABC has higher convergence speed which is increased by about 23% and better search accuracy.