Path-planning for uninhabited combat air vehicles (UCAV) is a typically complicated global optimization problem. It seeks a superior flight path in a complex battlefield environment, taking into various constraints. Many swarm intelligence (SI) algorithms have recently gained remarkable attention due to their capability to address complex optimization problems. However, different SI algorithms present various performances for UCAV path-planning since each algorithm has its own strengths and weaknesses. Therefore, this study provides an overview of different SI algorithms for UCAV path-planning research. In the experiment, twelve algorithms that published in major journals and conference proceedings are surveyed and then applied to UCAV path-planning. Moreover, to demonstrate the performance of different algorithms in further, we design different scales of problem cases for those comparative algorithms. The experimental results show that UCAV can find the safe path to avoid the threats efficiently based on most SI algorithms. In particular, the Spider Monkey Optimization is more effective and robust than other algorithms in handling the UCAV path-planning problem. The analysis from different perspectives contributes to highlight trends and open issues in the field of UCAVs.

中文翻译：

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UCAV路径规划问题的群智能算法比较研究

无人驾驶飞行器（UCAV）的路径规划是一个典型的复杂全球优化问题。考虑到各种限制因素，它在复杂的战场环境中寻求优越的飞行路径。由于其能够解决复杂的优化问题，许多群体智能（SI）算法最近都引起了人们的极大关注。但是，由于每种算法都有自己的优缺点，因此不同的SI算法在UCAV路径规划中表现出各种性能。因此，本研究概述了用于UCAV路径规划研究的不同SI算法。在实验中，调查了在主要期刊和会议论文集上发布的十二种算法，然后将其应用于UCAV路径规划。此外，为了进一步展示不同算法的性能，我们为那些比较算法设计了不同比例的问题案例。实验结果表明，基于大多数SI算法，UCAV可以找到有效的途径来避免威胁。特别地，蜘蛛猴优化在处理UCAV路径规划问题上比其他算法更有效，更强大。从不同角度进行的分析有助于突出UCAV领域的趋势和未解决的问题。