Kezhen Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Kezhen Liu has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Huan Liu
College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
1 shared publication - Yongqiang Dai
College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
1 shared publication
Latest Publications
- Open Access
- 3 January 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (1) , 396
Improvement of Dung Beetle Optimization Algorithm Application to Robot Path Planning
We propose the adaptive t-distribution spiral search Dung Beetle Optimization (TSDBO) Algorithm to address the limitations of the vanilla Dung Beetle Optimization Algorithm (DBO), such as vulnerability to local optima, weak convergence speed, and poor convergence accuracy. Specifically, we introduced an improved Tent chaotic mapping-based population initialization method to enhance the distribution quality of the initial population in the search space. Additionally, we employed a dynamic spiral search strategy during the reproduction phase and an adaptive t-distribution perturbation strategy during the foraging phase to enhance global search efficiency and the capability of escaping local optima. Experimental results demonstrate that TSDBO exhibits significant improvements in all aspects compared to other modified algorithms across 12 benchmark tests. Furthermore, we validated the practicality and reliability of TSDBO in robotic path planning applications, where it shortened the shortest path by 5.5–7.2% on a 10 × 10 grid and by 11.9–14.6% on a 20 × 20 grid.
- 3 January 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (1) , 396
Open AccessImprovement of Dung Beetle Optimization Algorithm Application to Robot Path Planning
We propose the adaptive t-distribution spiral search Dung Beetle Optimization (TSDBO) Algorithm to address the limitations of the vanilla Dung Beetle Optimization Algorithm (DBO), such as vulnerability to local optima, weak convergence speed, and poor convergence accuracy. Specifically, we introduced an improved Tent chaotic mapping-based population initialization method to enhance the distribution quality of the initial population in the search space. Additionally, we employed a dynamic spiral search strategy during the reproduction phase and an adaptive t-distribution perturbation strategy during the foraging phase to enhance global search efficiency and the capability of escaping local optima. Experimental results demonstrate that TSDBO exhibits significant improvements in all aspects compared to other modified algorithms across 12 benchmark tests. Furthermore, we validated the practicality and reliability of TSDBO in robotic path planning applications, where it shortened the shortest path by 5.5–7.2% on a 10 × 10 grid and by 11.9–14.6% on a 20 × 20 grid.