Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impact. Existing work has considered all efficiency and safety objectives for a single decision-maker (DM) and regarded this as a multiobjective optimization problem (MOP). However, there is usually not a single DM but two DMs, i.e., an efficiency DM and a safety DM, and the DMs are only concerned with their respective objectives. The final decision is made based on the solutions of both DMs. In this paper, for the first time, biparty multiobjective UAV path planning (BPMO-UAVPP) problems involving both efficiency and safety departments are modeled. The existing multiobjective immune algorithm with nondominated neighbor-based selection (NNIA), the hybrid evolutionary framework for the multiobjective immune algorithm (HEIA), and the adaptive immune-inspired multiobjective algorithm (AIMA) are modified for solving the BPMO-UAVPP problem, and then biparty multiobjective optimization algorithms, including the BPNNIA, BPHEIA, and BPAIMA, are proposed and comprehensively compared with traditional multiobjective evolutionary algorithms and typical multiparty multiobjective evolutionary algorithms (i.e., OptMPNDS and OptMPNDS2). The experimental results show that BPAIMA performs better than ordinary multiobjective evolutionary algorithms such as NSGA-II and multiparty multiobjective evolutionary algorithms such as OptMPNDS, OptMPNDS2, BPNNIA and BPHEIA.

翻译：无人机已广泛应用于城市任务，合理规划无人机路径既能提升任务效率，又能降低对第三方潜在冲击的风险。现有研究将所有效率与安全性目标视为单一决策者的问题，并将其建模为多目标优化问题。然而，实际通常存在两个决策者——即效率决策者与安全决策者——双方仅关注自身目标，最终决策需基于两者的解决方案共同确定。本文首次对涉及效率与安全部门的双目标多目标无人机路径规划问题进行了建模。通过对现有基于非支配邻域选择的多目标免疫算法、混合进化框架多目标免疫算法及自适应免疫启发多目标算法进行改进以求解BPMO-UAVPP问题，提出了双目标多目标优化算法BPNNIA、BPHEIA和BPAIMA，并与传统多目标进化算法及典型多党多目标进化算法（即OptMPNDS和OptMPNDS2）进行了全面比较。实验结果表明，BPAIMA优于普通多目标进化算法（如NSGA-II）及多党多目标进化算法（如OptMPNDS、OptMPNDS2、BPNNIA和BPHEIA）。