Shen Qianwen, Zhang Damin, He Qing, Ban Yunfei, Zuo Fengqin
School of Big Data and Information Engineering, Guizhou University, Guiyang, 550000, People's Republic of China.
Heliyon. 2024 Sep 4;10(17):e37286. doi: 10.1016/j.heliyon.2024.e37286. eCollection 2024 Sep 15.
Path planning for multiple unmanned aerial vehicles (UAVs) is crucial in collaborative operations and is commonly regarded as a complicated, multi-objective optimization problem. However, traditional approaches have difficulty balancing convergence and diversity, as well as effectively handling constraints. In this study, a directional evolutionary non-dominated sorting dung beetle optimizer with adaptive stochastic ranking (DENSDBO-ASR) is developed to address these issues in collaborative multi-UAV path planning. Two objectives are initially formulated: the first one represents the total cost of length and altitude, while the second represents the total cost of threat and time. Additionally, an improved multi-objective dung beetle optimizer is introduced, which integrates a directional evolutionary strategy including directional mutation and crossover, thereby accelerating convergence and enhancing global search capability. Furthermore, an adaptive stochastic ranking mechanism is proposed to successfully handle different constraints by dynamically adjusting the comparison probability. The effectiveness and superiority of DENSDBO-ASR are demonstrated by the constrained problem functions (CF) test, the Wilcoxon rank sum test, and the Friedman test. Finally, three sets of simulated tests are carried out, each including different numbers of UAVs. In the most challenging scenario, DENSDBO-ASR successfully identifies feasible paths with average values of the two objective functions as low as 637.26 and 0. The comparative results demonstrate that DENSDBO-ASR outperforms the other five algorithms in terms of convergence accuracy and population diversity, making it an exceptional optimization approach to path planning challenges.
多架无人机（UAV）的路径规划在协同作业中至关重要，通常被视为一个复杂的多目标优化问题。然而，传统方法在平衡收敛性和多样性以及有效处理约束方面存在困难。在本研究中，开发了一种具有自适应随机排序的定向进化非支配排序蜣螂优化器（DENSDBO - ASR）来解决协同多无人机路径规划中的这些问题。最初制定了两个目标：第一个目标代表长度和高度的总成本，而第二个目标代表威胁和时间的总成本。此外，引入了一种改进的多目标蜣螂优化器，它集成了包括定向变异和交叉的定向进化策略，从而加速收敛并增强全局搜索能力。此外，还提出了一种自适应随机排序机制，通过动态调整比较概率来成功处理不同的约束。通过约束问题函数（CF）测试、威尔科克森秩和检验和弗里德曼检验证明了DENSDBO - ASR的有效性和优越性。最后，进行了三组模拟测试，每组包括不同数量的无人机。在最具挑战性的场景中，DENSDBO - ASR成功识别出可行路径，两个目标函数的平均值分别低至637.26和0。比较结果表明，DENSDBO - ASR在收敛精度和种群多样性方面优于其他五种算法，使其成为解决路径规划挑战的一种出色的优化方法。
