针对无人机路径规划中效率与安全双决策者矛盾，提出新型多目标优化方法。
Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons
- 将效率与安全分别视为两个独立决策者，构建双主体多目标模型。
- 所提BPAIMA算法在10次实验中平均收敛速度提升23%，解集分布更优。
- 适合需要权衡效率与安全的智能交通、应急救援等场景使用。
无人机广泛应用于城市任务中，合理规划路径可提升任务效率并降低对第三方的潜在风险。现有研究通常将效率与安全统一视为单一决策者的多目标优化问题（MOP），但实际中往往存在两个决策者——效率决策者与安全决策者，各自关注自身目标，最终决策基于双方解集的协调。本文首次建模了涉及效率与安全部门的双主体多目标无人机路径规划（BPMO-UAVPP）问题。对现有的非支配邻域选择多目标免疫算法（NNIA）、混合进化框架（HEIA）及自适应免疫启发式多目标算法（AIMA）进行改进，提出三种双主体多目标优化算法：BPNNIA、BPHEIA与BPAIMA。在对比传统多目标进化算法（如NSGA-II）及典型多主体多目标进化算法（OptMPNDS、OptMPNDS2）的实验中，结果表明BPAIMA在10次独立实验中平均收敛速度提升23%，且解集分布性能最优，显著优于其他算法。
原文摘要 · Abstract (English)
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.
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