Xuanshuo Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xuanshuo Zhang has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Ce Guo
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
1 shared publication - Kunpeng Xu
College of Mechanical and Electrical Engineering, Xuzhou College of Industrial Technology, Xuzhou 221140, China
1 shared publication - Longhai Li
School of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221018, China
1 shared publication - Xu Zhang
School of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221018, China
1 shared publication - Yizheng Ge
School of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221018, China
1 shared publication - Yue Chen
School of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221018, China
1 shared publication
Latest Publications
- Open Access
Improved Sparrow Search Algorithm Based on Multistrategy Collaborative Optimization Performance and Path Planning Applications
To address the problems of limited population diversity and a tendency to converge prematurely to local optima in the original sparrow search algorithm (SSA), an improved sparrow search algorithm (ISSA) based on multi-strategy collaborative optimization is proposed. ISSA employs three strategies to enhance performance: introducing one-dimensional composite chaotic mapping SPM to generate the initial sparrow population, thus enriching population diversity; introducing the dung beetle dancing search behavior strategy to strengthen the algorithm’s ability to jump out of local optima; integrating the adaptive t-variation improvement strategy to balance global exploration and local exploitation capabilities. Through experiments with 23 benchmark test functions and comparison with algorithms such as PSO, GWO, WOA, and SSA, the advantages of ISSA in convergence speed and optimization accuracy are verified. In the application of robot path planning, compared with SSA, ISSA exhibits shorter path lengths, fewer turnings, and higher planning efficiency in both single-target point and multi-target point path planning. Especially in multi-target point path planning, as the obstacle rate increases, ISSA can more effectively find the shortest path. Its traversal order is different from that of SSA, making the planned path smoother and with fewer intersections. The results show that ISSA has significant superiority in both algorithm performance and path planning applications.
Open AccessImproved Sparrow Search Algorithm Based on Multistrategy Collaborative Optimization Performance and Path Planning Applications
To address the problems of limited population diversity and a tendency to converge prematurely to local optima in the original sparrow search algorithm (SSA), an improved sparrow search algorithm (ISSA) based on multi-strategy collaborative optimization is proposed. ISSA employs three strategies to enhance performance: introducing one-dimensional composite chaotic mapping SPM to generate the initial sparrow population, thus enriching population diversity; introducing the dung beetle dancing search behavior strategy to strengthen the algorithm’s ability to jump out of local optima; integrating the adaptive t-variation improvement strategy to balance global exploration and local exploitation capabilities. Through experiments with 23 benchmark test functions and comparison with algorithms such as PSO, GWO, WOA, and SSA, the advantages of ISSA in convergence speed and optimization accuracy are verified. In the application of robot path planning, compared with SSA, ISSA exhibits shorter path lengths, fewer turnings, and higher planning efficiency in both single-target point and multi-target point path planning. Especially in multi-target point path planning, as the obstacle rate increases, ISSA can more effectively find the shortest path. Its traversal order is different from that of SSA, making the planned path smoother and with fewer intersections. The results show that ISSA has significant superiority in both algorithm performance and path planning applications.