Simulation experiments 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-c...
An efficient and robust algorithm for autonomous three-dimensional path planning of UAVs in complex obstacle environments is presented and coordinated improvements realize targeted optimization for UAV 3D flight characteristics.
The Personal History Memory Mechanism is introduced, which replaces random perturbation with weighted historical experience to enhance the directional search capability of the algorithm, and a greedy selection strategy is embedded to ensure the monotonically non-deteriorating quality of population solutions.
: To address the challenges of high search dimensionality, complex constraints, and susceptibility to local optima in multi-Unmanned Aerial Vehicle (UAV) cooperative path planning within complex 3D environments, an improved t-distribution-based chaotic Black-winged Kite Algorithm (ITCBKA) is proposed and applied to thr...
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.
Aiming at the multi-objective path planning problem when multiple agents cooperatively search for maneuvering targets in complex three-dimensional environments, this paper proposes an improved 3D Artificial Bee Colony algorithm (MOABC) based on the multi-objective Pareto optimization framework. The proposed method trea...
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