A Comparative Study of Swarm Intelligence Algorithms for UCAV Path-Planning Problems

Haoran Zhu, Yunhe Wang, Zhiqiang Ma and Xiangtao Li
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Haoran Zhu: School of Artificial Intelligence, Jilin University, Changchun 130012, China
Yunhe Wang: School of Computer Science and Technology, Northeast Normal University, Changchun 130117, China
Zhiqiang Ma: School of Computer Science and Technology, Northeast Normal University, Changchun 130117, China
Xiangtao Li: School of Artificial Intelligence, Jilin University, Changchun 130012, China

Mathematics, 2021, vol. 9, issue 2, 1-31

Abstract: 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.

Keywords: swarm intelligence; UCAV path-planning; optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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