The Traveling Salesman Problem (TSP) is a classic problem in combinatorial optimization, aiming to find the shortest path that traverses all cities and eventually returns to the starting point. The ant colony optimization algorithm has achieved significant results, but when the number of cities increases, the ant colony algorithm is prone to fall into local optimal solutions, making it difficult to obtain the global optimal path. To overcome this limitation, this paper proposes an innovative hybrid ant colony algorithm. Our main motivation is to introduce other optimization strategies to improve the global search ability and convergence speed of the ant colony algorithm in solving TSP problems. We first incorporate the iterative solution of the sparrow search algorithm (SSA) into the ant colony algorithm to provide a better initial pheromone distribution. Second, we improve the pheromone update method to enhance the algorithm’s diversity during the search process and reduce the risk of falling into local optima. Finally, we define a dynamic pheromone evaporation factor to adjust the pheromone evaporation rate according to real-time changes in the search process. Through simulation tests on large-scale TSP problems and practical applications, we find that the hybrid ant colony algorithm outperforms the ant colony algorithm in both accuracy and running time. In Eg.2, the average accuracy of ISSA-ACO is improved by 12%, and the average running time is reduced by 45.6%. This study not only provides a new and effective method for solving large-scale TSP problems but also provides valuable references and insights for the application of ant colony algorithms in solving other complex optimization problems. At the same time, our research further verifies the effectiveness of improving heuristic algorithms by fusing different optimization strategies, providing new ideas and directions for future algorithm design and optimization.

Most existing tracking methods try to represent the target by exploiting visual information as much as possible based on the various deep networks. However, the appearance model hardly describes the attribute feature of the target well, which makes the trackers fail to adapt to the complex visual surrounding. In this paper, inspired by brain-like intelligence, we propose an One-stream Vision-Language Memory network (OVLM) for object tracking. Firstly, we use the combination of vision and language to build the target model and use the semantic information in the language to compensate for the instability of visual information, making the target model more stable in the face of complex appearance changes. Secondly, to build a more compact target model, we propose a memory token selection mechanism that utilizes linguistic information to eliminate tokens that do not contain target information. Furthermore, to provide better visual information for target modeling, we propose a language-based evaluation method to select high-quality target samples to be stored in the memory. Finally, OVLM achieves a 64.7% success rate on the large-scale tracking benchmark dataset TNL2K, outperforming the previous best result (VLT) by 11.6%. By exposing the possibility of the vision-language memory network, we aim to draw greater attention to it and open up new avenues for vision-language tracking.

Purpose: This paper aims to improve the generalization capability of feature extraction scheme by introducing a micro-cracks detection method based on self-learning features. Micro-cracks detection of multicrystalline solar cell surface based on machine vision is fast, economical, intelligent and easier for on-line detection. However, the generalization capability of feature extraction scheme adopted by existed methods is limited, which has become an obstacle for further improving the detection accuracy.Design/methodology/approach: A novel micro-cracks detection method based on self-learning features and low-rank matrix recovery is proposed in this paper. First, the input image is preprocessed to suppress the noises and remove the busbars and fingers. Second, a self-learning feature extraction scheme in which the feature extraction templates are changed along with the input image is introduced. Third, the low-rank matrix recovery is applied to the decomposition of self-learning feature matrix for obtaining the preliminary detection result. Fourth, the preliminary detection result is optimized by incorporating the superpixel segmentation. Finally, the optimized result is further fine-tuned by morphological postprocessing.Findings: Comprehensive evaluations are implemented on a data set which includes 120 testing images and corresponding human-annotated ground truth. Specifically, subjective evaluations show that the shape of detected micro-cracks is similar to the ground truth, and objective evaluations demonstrate that the proposed method has a high detection accuracy.Originality/value: First, a self-learning feature extraction method which has good generalization capability is proposed. Second, the low-rank matrix recovery is combined with superpixel segmentation for locating the defective regions.

Numerous tracking approaches attempt to improve target representation through target-aware or distractor-aware. However, the unbalanced considerations of target or distractor information make it diffcult for these methods to benefit from the two aspects at the same time. In this paper, we propose a target-distractor aware model with discriminative enhancement learning loss to learn target representation, which can better distinguish the target in complex scenes. Firstly, to enlarge the gap between the target and distractor, we design a discriminative enhancement learning loss. By highlighting the hard negatives that are similar to the target and shrinking the easy negatives that are pure background, the features sensitive to the target or distractor representation can be more conveniently mined. On this basis, we further propose a target-distractor aware model. Unlike existing methods of preference target or distractor, we construct the target-specific feature space by activating the target-sensitive and the distractor-silence feature. Therefore, the appearance model can not only represent the target well but also suppress the background distractor. Finally, the target-distractor aware target representation model is integrated with a Siamese matching network for visual tracking for achieving robust and realtime visual tracking. Extensive experiments are performed on eight tracking benchmarks show that the proposed algorithm achieves favorable performance.