Path Planning Based on the Improved Butterfly Optimization Algorithm

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.