IETO: An Improved Exponential–Trigonometric Optimisation Algorithm for Dynamic 3D UAV Trajectory Planning

Abstract

Three-dimensional unmanned aerial vehicle (UAV) path planning in cluttered terrain requires a planner to search a continuous waypoint space, whereas the actual safety of a candidate can be assessed only after decoding the waypoint vector into a flight path. The decoded path must satisfy clearance, no-fly-zone, altitude, segment-length and turning constraints, which makes the practical fitness landscape nonconvex, nonsmooth and strongly affected by narrow feasible corridors. This paper therefore studies UAV trajectory generation through a finite-budget derivative-free planning framework. An improved Exponential–Trigonometric Optimisation algorithm (IETO) is proposed for this real-coded UAV waypoint model. The method combines optimised Latin hypercube sampling for initial spatial coverage, adaptive exploration–exploitation control for search-radius regulation and probabilistic local refinement for terminal path improvement. Candidate paths are ranked by a nonsmooth exterior-penalty fitness and are finally accepted only after explicit decoded-path feasibility checks. Benchmark tests and deterministic, uncertain and long-range UAV scenarios show that IETO produces high-quality feasible trajectories and achieves the lowest mean planning fitness in the reported main UAV comparison under the same evaluation budget.