Concerning the problems that basic Ant Colony Optimization (ACO) is easy to fall into the local optimum, and has too long path and excessive turning angles during path search, an improved ACO algorithm based on turning angle constraint was proposed. Firstly, the initial pheromone concentration of the area between the starting point and the target point was enhanced to avoid the initial blind search. Then, the A ^* algorithm's evaluation function and the turning angle constraint factor were added to the heuristic function. In this way, the node with the shortest path length and least number of turns was able to be selected at the next step. Finally, the distribution principle of wolf pack algorithm was introduced in the pheromone updating part to enhance the influence of high-quality population. At the same time, the Max and Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid the algorithm being trapped into the local optimum. Matlab simulation showed that compared with the traditional ACO, the improved algorithm was able to shorten the planned path length by 13.7%, reduce the number of turns by 64.3% and decrease the accumulated turning angle by 76.7%. Experimental results show that the improved ACO algorithm can effectively solve the global path planning problem and avoid the excessive energy loss of mobile robots.

Bio-inspired Deep Neural Network (DNN) is a revolutionary breakthrough in artificial intelligent field. However, the lack of storage space as well as computing capacity caused by the explosive increase of the model weights not only keeps DNN apart from its original inspiration, but also makes it difficult to deploy DNN on embedded/mobile devices. In order to solve this problem, the biological selection principle in the evolution was studied, and a novel neural network algorithm based on "evolution" + "randomness" + "selection" was proposed. In this method, the size of the existing models were greatly simplified on the premise of maintaining the basic framework of the existing neural network models. First, the weight parameters were clustered. Then, based on the cluster centroid values of the parameters, the random perturbation was added to reconstruct the parameters. Finally, the image classification and object detection were performed on the reconstructed model to realize the accuracy test and model stability analysis. Experimental results on ImageNet dataset and COCO dataset show that the proposed model reconstruction method can compress the sizes of four models, including Darknet19, ResNet18, ResNet50 and YOLOv3, to 1/4-1/3 of the original ones, and under the condition of 1%-3% performance improvement in the test accuracy of image classification and object detection, there is the possibility of further simplification.