Year: 2025
Type: conference-paper
Abstract: Literature [1] introduces an adaptive weight strategy to accelerate population convergence while enabling the algorithm to escape local optima in a timely manner. Literature [4] uses Tent chaotic mapping and refractive reverse learning to enhance the diversity of the initial population, balancing the algorithm's global search and local exploitation capabilities. Literature [6] designs an algorithm to identify irregular U-shaped valleys, ultimately improving the search efficiency of the A* algorithm. However, traditional algorithms[2] have difficulty adapting to dynamic environments and require re-computation of mathematically planned algorithms when encountering sudden obstacles. Therefore, to address the above issues, this paper proposes a new method[3] based on the Improved Butterfly Optimization Algorithm (IBOA) applied to the dynamic path planning of unmanned aerial vehicles. The original butterfly algorithm is improved by using the normal cloud mutation strategy[5], triangular random walk strategy, and non-uniform mutation strategy.
Authors: Yutian Su, Wendang Jiang
Institution: Jiangsu University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414128269