Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons

IF 5.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-12 DOI:10.1109/TETCI.2024.3361755

Kesheng Chen;Wenjian Luo;Xin Lin;Zhen Song;Yatong Chang

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Abstract

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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进化双方多目标无人机路径规划：问题与经验比较

无人飞行器（UAV）已被广泛应用于城市任务中，合理规划无人飞行器路径可提高任务效率，同时降低潜在第三方影响的风险。现有工作考虑了单个决策者（DM）的所有效率和安全目标，并将其视为多目标优化问题（MOP）。然而，通常情况下并不是只有一个 DM，而是有两个 DM，即效率 DM 和安全 DM，而且 DM 只关注各自的目标。最终决策是根据两个 DM 的解决方案做出的。本文首次模拟了同时涉及效率和安全两个部门的两方多目标无人机路径规划（BPMO-UAVPP）问题。为了解决 BPMO-UAVPP 问题，本文对现有的基于非支配邻域选择的多目标免疫算法（NNIA）、多目标免疫算法的混合进化框架（HEIA）和自适应免疫启发多目标算法（AIMA）进行了改进、然后提出了包括 BPNNIA、BPHEIA 和 BPAIMA 在内的两方多目标优化算法，并将其与传统多目标进化算法和典型的多方多目标进化算法（即 BPNNIA、BPHEIA 和 BPAIMA）进行了综合比较。e.,OptMPNDS 和 OptMPNDS2）进行了综合比较。实验结果表明，BPAIMA 的性能优于普通多目标进化算法（如 NSGA-II）和多方多目标进化算法（如 OptMPNDS、OptMPNDS2、BPNNIA 和 BPHEIA）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Emerging Topics in Computational Intelligence Mathematics-Control and Optimization

CiteScore

10.30

自引率

7.50%

发文量

147

期刊介绍： The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys. TETCI is an electronics only publication. TETCI publishes six issues per year. Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.

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