Although bird swarm optimization (BSA) algorithm shows excellent performance in solving continuous optimization problems, it is not an easy task to apply it solving the combination optimization problem such as traveling salesman problem (TSP). Therefore, this paper proposed a novel discrete BSA based on information entropy matrix (DBSA) for TSP. Firstly, in the DBSA algorithm, the information entropy matrix is constructed as a guide for generating new solutions. Each element of the information entropy matrix denotes the information entropy from city i to city j. The higher the information entropy, the larger the probability that a city will be visited. Secondly, each TSP path is represented as an array, and each element of the array represents a city index. Then according to the needs of the minus function proposed in this paper, each TSP path is transformed into a Boolean matrix which represents the relationship of edges. Third, the minus function is designed to evaluate the difference between two Boolean matrices. Based on the minus function and information entropy matrix, the birds’ position updating equations are redesigned to update the information entropy matrix without changing the original features. Then three TSP operators are proposed to generate new solutions according to the updated information entropy matrix. Finally, the performance of DBSA algorithm was tested on a large number of benchmark TSP instances. Experimental results show that DBSA algorithm is better or competitively outperforms many state-of-the-art metaheuristic algorithms.

Simulated annealing (SA) algorithm is a popular intelligent optimisation algorithm, but its efficiency is unsatisfactory. To improve its efficiency, this paper presents a swarm SA (SSA) algorithm by exploiting the learned knowledge from searching history. In SSA, a swarm of individuals run SA algorithm collaboratively. Inspired by ant colony optimisation (ACO) algorithm, SSA stores knowledge in construction graph and uses the solution component selection scheme of ACO algorithm to generate candidate solutions. Candidate list with bounded length is used to speed up SSA. The effect of knowledge-based sampling is verified on benchmark travelling salesman problems. Comparison studies show that SSA algorithm has promising performance in terms of convergence speed and solution accuracy.

The automatic segmentation technique for colorectal polyps in colonoscopy is considered critical for aiding physicians in real-time lesion identification and minimizing diagnostic errors such as false positives and missed lesions. Despite significant progress in existing research, accurate segmentation of colorectal polyps remains technically challenging due to persistent issues such as low contrast between polyps and mucosa, significant morphological heterogeneity, and susceptibility to imaging artifacts caused by bubbles in the colorectal lumen and poor lighting conditions. To address these limitations, this study proposed a novel pyramid vision transformer-based hierarchical path aggregation network (HPANet) for polyp segmentation. Specifically, firstly, the backward multi-scale feature fusion module (BMFM) was developed to enhance the ability of processing polyps with different scales. Secondly, the forward noise reduction module (FNRM) was designed to learn the texture features of the upper and lower layers to reduce the influence of noise such as bubbles. Finally, in order to solve the problem of boundary ambiguity caused by repeated up and down sampling, the boundary feature refinement module (BFRM) was developed to further refine the boundary. The proposed network was compared with several representative networks on five public polyp datasets. Experimental results show that the proposed network achieves better segmentation performance, especially on the Kvasir SEG dataset, where the mDice and mIoU coefficients reach 0.9204 and 0.8655.