Intelligent Optimization Algorithms for Multi-UAV Path Planning: A Comprehensive Review

This work was supported by the National Social Science Fund of China under Grant 2023-SKJJ-B-117, in part by the Natural Science Foundation of Shaanxi Province under Grant 2024JC-YBMS-529, in part by the Fund for technical areas of infrastructure strengthening plan projects under Grant 2023-JCJQ-JJ-0772 and in part by the Equipment Comprehensive Research Project under Grant WJ2023B020400-4. The deployment of multi-UAV systems for frontline and complex combat missions has become a dominant trend in intelligent warfare. As operational airspace extends from medium-high to low and ultra-low altitudes, combined with global efforts to strengthen multi-layered air defense systems, the increasingly dense and complex battlefield environments impose rigorous demands on multi-UAV path planning. Serving as the cornerstone of collaborative mission coordination, path planning is critical for maximizing combat effectiveness through comprehensive analysis of adversarial and operational constraints. This paper synthesizes existing reviews and scientific literature to establish a systematic framework for multi-UAV path planning. We analyze current research progress through four key dimensions: 1) battlefield environment modeling, 2) constraint formulations and objective functions, 3) intelligent optimization algorithms, and 4) collaborative path optimization. Highlighting the Wolf Pack Algorithm's (WPA) exceptional performance in resolving high-dimensional, multi-peak optimization challenges, we focus on its advancements and applications in multi-UAV path planning. Finally, we project future development directions aligned with the evolution of unmanned and intelligent warfare.