Unmanned combat aerial vehicle path planning in complex environment using multi-strategy sparrow search algorithm with double-layer coding

Abstract

Unmanned combat aerial vehicles (UCAV) path planning in complex environments demands a substantial number of path points to determine feasible paths. Establishing an effective flight path for UCAVs requires numerous path points to account for fuel constraints, artillery threats, and radar avoidance. This increase in path points raises the dimensionality of the problem, which in turn degrades algorithm performance. To mitigate this issue, a double-layer coding (DLC) model is utilized to remove redundant path points, consequently lowering computational complexity and operational difficulties. Meanwhile, this paper introduces a novel enhanced sparrow search algorithm (MESSA) based on multi-strategy for UCAV path planning. The MESSA incorporates a novel dynamic fitness regulation learning strategy (DFRL), a random differential learning strategy (RDL), an elite example equilibrium learning strategy (EEEL), a dynamic elimination and regeneration strategy based on the elite example (DERE), and quadratic interpolation (QI). Furthermore, MESSA is compared against 11 state-of-the-art algorithms, demonstrating exceptional optimization performance and robustness. Additionally, the combination of MESSA with the DLC model (DLC-MESSA) is applied to solve the UCAV path planning problem. The experimental results from five complex environments indicate that DLC-MESSA outperforms other algorithms in 80% of the cases by achieving the lowest average cost, thereby demonstrating its superior robustness and computational efficiency.