In this paper, we propose a new ant colony optimization algorithm, called learning-based neural ant colony optimization (LN-ACO), which incorporates an "intelligent ant". This intelligent ant contains a convolutional neural network pre-trained on a large set of instances which is able to predict the selection probabilities of the set of possible choices at each step of the algorithm. The intelligent ant is capable of generating a solution based on knowledge learned during training, but also guides other 'traditional' ants in improving their choices during the search. As the search progresses, the intelligent ant is also influenced by the pheromones accumulated by the colony, leading to better solutions. The key idea is that if tasks or instances share common features either in terms of their search landscape or solutions, then information learned by solving one instance can be applied to substantially accelerate the search on another. We evaluate the proposed algorithm on two public datasets and one real-world test set in the path planning domain. The results demonstrate that LN-ACO is competitive in its search capability compared to other ACO methods, with a significant improvement in convergence speed. CCS CONCEPTS• Computing methodologies → Search methodologies.

Food sampling programs are implemented from time to time in local areas or throughout the country in order to guarantee food safety and to improve food quality. The hidden patterns in the accumulated huge amount of data and their potential values are worthy to research. In this paper, Extreme learning machine (ELM) is employed on real data sets collected from the food safety inspections of China in recent two years, in order to mine the relationship between food quality and food category, manufacturing site and season, inspection site and season, and many other attributes. Experimental results indicate that the ELM approach has better prediction precision and generalization ability than Logistic regression that was adopted in preceding work. The patterns obtained are helpful for making more effective food sampling plans and for more targeted food safety tracing.

To solve the problem of joint calibration in multi-sensor information fusion, a joint calibration technique based on three-dimensional lidar point cloud data and two-dimensional gray image data is proposed. Firstly, by extracting the corner information of the gray image data, the two-dimensional coordinates of the corner were obtained, and the calibration of the monocular camera was completed by using the corner information, and its internal and external parameters were obtained. Then, by extracting the corner information of the point cloud data obtained by lidar, the corresponding corner points are matched. Finally, the rotation and translation matrix from lidar coordinate system to image coordinate system is generated to realize the joint calibration of lidar and camera.