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

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

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

Research on multi-objective path planning for UAVs in complex environments based on improved adaptive genetic algorithm

With the popularization of unmanned aerial vehicles (UAVs) in scenarios such as military reconnaissance, logistics transportation, and post-disaster rescue, Generating optimal flight paths that guarantee both safety and timeliness amidst high-density barriers and unknown environmental factors presents a formidable challenge in autonomous navigation. Standard genetic algorithms are often hindered by premature convergence, suboptimal local solutions, and diminished genetic diversity in later iterations. In response to these challenges, we propose an Improved Adaptive Genetic Algorithm (IAGA) that synergizes an adaptive mutation strategy with an elite retention framework.This method first constructs a comprehensive cost model combining path length and obstacle threat potential field based on the grid concept, thereby converting the nonlinear path planning problem into a computable mathematical form; secondly, it designs a linearly decreasing adaptive mutation operator that can dynamically adjust the mutation probability according to the population evolution stage, thus maintaining a strong global search ability in the early stages of the algorithm and strengthening local fine search in the later stages; finally, it introduces an elite retention strategy to ensure that excellent individuals are retained and participate in subsequent evolution, avoiding population degradation. Simulation experiments conducted in a Python environment show that in a complex obstacle environment of 100m × 100m× 100m, the IAGA algorithm can effectively plan the optimal path, and the convergence speed is significantly improved compared to traditional genetic algorithms, verifying the effectiveness and robustness of this method in handling multi-constraint path planning problems.

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.

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

To address the tendency to fall into local optima, insufficient convergence accuracy, and path-quality fluctuations in three-dimensional UAV path planning under complex terrain and multiple constraints, this study proposes a hybrid improved Grey Wolf Optimization algorithm, termed HLGWO. A unified objective function is first constructed by considering path length, safety risk, flight altitude, turning smoothness, and terrain complexity, and an adaptive weighting mechanism is introduced to meet the requirements of different flight stages. Within the standard GWO framework, Latin Hypercube Sampling is used to improve the initial population distribution, Gaussian random walk is incorporated to enhance local search capability, and a Differential Evolution operator is introduced to promote information exchange and refined exploitation among individuals. Experiments on the CEC2005 and CEC2020 benchmark suites, together with eight real DEM-based UAV flight scenarios, 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.

Dynamic-robustness-oriented hierarchical task planning for UAV swarm based on improved pigeon-inspired optimization

Simulation results show that the proposed hierarchical task planning framework significantly outperforms traditional approaches in efficiency, robustness, and scalability, highlighting its strong potential for UAV swarm mission planning in complex environments.