基于人工免疫的无人机航迹规划
UAV Path Planning Based on Artificial Immunity

针对传统遗传算法在无人机航迹规划问题中的效率低下和结果不一致性，本研究提出了一种创新的基于人工免疫的遗传算法(genetic algorithm based on artificial immunity, AIGA)。AIGA通过建立动态疫苗库，根据迭代次数动态调整，增强了对不良解的识别与淘汰，有效提升了路径规划的质量和效率。本算法与多种进化算法在路径优化和无人机航迹规划问题上进行了比较，实验结果证明，AIGA能为无人机生成安全且经济的飞行路径，并在路径优化问题上显示出与其他启发式算法相比的显著优势，显示出广阔的应用潜力。
To address the inefficiencies and inconsistencies associated with traditional genetic algorithms in UAV path planning, this study introduces an innovative approach known as the Genetic Algorithm Based on Artificial Immunity (AIGA). AIGA enhances the identification and elimination of inferior solutions by employing a dynamic vaccine repository that adjusts according to the number of iterations, thereby significantly improving the quality and efficiency of path planning. Comparative experiments with various evolutionary algorithms on path optimization and UAV path planning problems have demonstrated that AIGA can generate safe and economical flight paths for UAVs. It shows a distinct advantage over other heuristic algorithms in path optimization issues, indicating its broad potential for application.