A multi-strategy improved dung beetle optimization algorithm for UAV path planning

UAV path planning optimization algorithm based on an improved dung beetle optimization algorithm with a mixed strategy

The Circle-SPM chaotic map is introduced to optimize the population initialization process, effectively mitigating the premature convergence caused by uneven distribution and a lack of population diversity.

A hybrid improved Grey Wolf optimization algorithm for three dimensional UAV path planning in complex terrain

Experiments show that HLGWO generally outperforms several comparison algorithms in convergence accuracy, stability, and path cost, thereby improving the safety, feasibility, and optimization performance of 3D UAV path planning in complex environments.

Adaptive Two-Stage Pigeon-Inspired Optimization Algorithm for UAV Three-Dimensional Path

To address slow convergence, local optimum stagnation, and multi-objective imbalance problems for unmanned aerial vehicle (UAV) three-dimensional (3D) path planning in complex obstacle environments, an improved adaptive two-stage pigeon swarm optimization (IPIO) algorithm is proposed. Firstly, a hybrid initialization strategy integrating Latin hypercube sampling and obstacle avoidance constraints is adopted to improve initial population diversity and the quality of feasible solutions. Secondly, in the map compass stage, a linearly decreasing adaptive map factor and population diversity-based dynamic perturbation strategy are introduced to balance global exploration and local exploitation while preventing premature convergence. In the landmark stage, an inverse fitness weighting elite center updating mechanism and linearly decreasing elite quantity strategy are designed to enhance the guidance of high-quality individuals and accelerate convergence. A multi-objective fitness function integrating path length, obstacle avoidance safety, and flight smoothness is constructed, whose weight coefficients (ωL=0.3, ωC=0.5, ωS=0.2) are calibrated through parameter-sensitivity analysis and Pareto frontier comparison across six representative weight combinations. Combining ablation validation for each improved module, single-UAV multi-scenario tests, and preliminary multi-UAV trials, these coordinated improvements realize targeted optimization for UAV 3D flight characteristics. Specifically, the preliminary multi-UAV trials involve three UAVs performing independent trajectory planning in shared obstacle environments without explicit inter-UAV collision avoidance constraints, and the reported improvements are based on single-UAV experiments. Finally, comparative experiments are conducted with a standard 100 × 100 × 50 m space, and varying obstacle densities are demonstrated in six diverse 3D test scenarios, where the proposed IPIO achieves an average path length reduction of 12.8% and 15.3% compared to the standard PIO and PSO, respectively. The average fitness improvement is 14.2% over PIO, 16.8% over PSO, 19.5% over GWO, 24.1% over CO, and 38.7% over CS. Key path-quality metrics include a minimum obstacle clearance of 2.37 m, average smoothness cost of 0.34, average convergence time of 0.60 s, and computational cost of O(N*D*MaxIter). Statistical tests confirm that these improvements are significant (p < 0.05) in all tested scenarios. This study presents an efficient and robust algorithm for autonomous three-dimensional path planning of UAVs in complex obstacle environments.

3D Path Planning for UAVs Based on Improved Red Kite Optimization Algorithm in Complex Threat Environments

Aiming at the problems of slow convergence speed, low optimization accuracy, susceptibility to local optima, and insufficient stability of the traditional Red Kite Optimization Algorithm (ROA) for unmanned aerial vehicle (UAV) path planning in complex three-dimensional environments, this paper proposes an Improved Red Kite Optimization Algorithm (IROA). Firstly, the Personal History Memory Mechanism (PHMM) is introduced, which replaces random perturbation with weighted historical experience to enhance the directional search capability of the algorithm. Secondly, a cosine-based nonlinear adaptive weight strategy is adopted to dynamically balance global exploration and local exploitation. Finally, a greedy selection strategy is embedded to ensure the monotonically non-deteriorating quality of population solutions. In the experimental section, the performance of the improved algorithm is first verified using benchmark test functions, and then four types of UAV flight path-planning scenarios are constructed in the simulation environment for comparison. The results demonstrate that IROA outperforms ROA, WOA, HHO, DBO, WHO, and other algorithms in terms of optimal value, mean value, and stability indicators. Its convergence speed and optimization accuracy are significantly improved, and high-quality paths can be generated even in obstacle scenarios of varying complexity, which verifies the feasibility of the proposed algorithm for three-dimensional UAV path planning.

A Reinforcement Learning-Based Multi-Strategy Differential Evolution Algorithm for Agricultural UAV Path Planning

In the realm of precision agriculture, agricultural UAV path planning is challenging because the UAV must avoid obstacles, follow uneven terrain, and satisfy multiple flight constraints simultaneously. Differential evolution (DE) has been widely adopted for this problem because of its simple structure and effective optimization capability. However, existing DE-based methods often become trapped in local optima and cannot effectively balance exploration and exploitation in complex search environments. To address these issues, this paper proposes a reinforcement learning-based multi-strategy differential evolution algorithm, named PPOMSDE. By introducing Proximal Policy Optimization (PPO) to construct a multi-dimensional state pool and an action pool, PPOMSDE enables adaptive strategies for individuals, improving strategy selection during the search process. An independent multi-buffer is adopted to ensure strict data isolation and efficient learning to avoid strategy confusion. In addition, an adaptive triplet mechanism which partitions the population into fitness-based tiers (best, medium, and worst) assigns different control parameters and mutation strategies to individuals with different fitness levels, improving the balance between global exploration and local exploitation. Extensive experiments on the CEC’2014 and CEC’2017 benchmark suites demonstrate the effectiveness of PPOMSDE. The proposed method achieves the lowest average performance ranks of 1.39 on the combined 10-D and 30-D CEC’2014 benchmarks and 1.03 on the 10-D CEC’2017 benchmarks. In agricultural UAV path planning, PPOMSDE generates safer and smoother flight paths while maintaining accurate terrain-following flight, reducing the overall cost by an average of 22.42% compared with ISDE, L-SHADE, SHADE, and ISHACDE.

3D UAV path planning and global optimization based on multi-strategy enhanced snake optimizer