AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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.
Navigation mark inspections are critical to ensuring maritime navigation safety, and the efficiency of unmanned aerial vehicle (UAV) inspection path planning directly affects inspection costs and operations. This problem is formulated as a Traveling Salesman Problem (TSP), and the traditional Grey Wolf Optimizer (GWO) has limitations in discrete optimization, including weak search capabilities, simple neighborhood structures, and poor local optimization. To address these issues, this paper proposes an improved Grey Wolf Optimizer (IGWO). First, this paper introduces three neighborhood search operators: reverse, insertion, and swap. Second, an adaptive step size mechanism based on Euclidean distance is designed. Third, the 3-opt local optimization algorithm is integrated. Finally, experiments are conducted using real navigation mark data from Pingtan and Tianjin, and IGWO is compared with traditional algorithms. Results show that IGWO effectively adapts GWO to discrete spaces and achieves optimal paths across datasets of varying scales. Its path length reduction rates improve by 0.51% to 58.01% over the other seven algorithms. These findings provide efficient UAV path planning solutions for navigation mark inspection and offer technical support for smart maritime supervision systems.
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.
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.
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