TL;DR
Experimental results demonstrate that the proposed ACO-initialized Greedy and Escape PSO consistently achieves superior optimization performance, producing shorter and smoother flight paths with faster and more stable convergence.
To address the issues of slow convergence and susceptibility to local optima when applying traditional artificial fish swarm algorithms to 3D path planning for unmanned aerial vehicles (UAVs), this paper proposes an improved adaptive artificial fish swarm algorithm (IAFSA). A simulation environment incorporating undula...
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.
This work develops an enhanced PSO with adaptive weight and Gaussian mutation (EPSO-AWGM) that can obtain shorter and higher-quality flight paths and has great application potential in practical UAVs path planning scenarios.
The Improved Dhole Optimization Algorithm is proposed, which enhances the original DOA framework by integrating a logistic-map-based chaotic mapping, a dynamic chaotic perturbation mechanism, and an adaptive stage-division strategy, and significantly outperforms the original DOA in terms of convergence speed and final...
An enhanced PSO with adaptive weight and Gaussian mutation (EPSO-AWGM) that can obtain shorter and higher-quality flight paths and has great application potential in practical UAV path planning scenarios.
An improved Whale Optimization Algorithm (R*WOA) that integrates the Rapidly Expanding Random Tree Star (RRT*) algorithm that significantly outperforms traditional WOA, GA and HHO algorithms, enabling the planning of optimal UAV flight trajectories with shorter paths, higher safety and better smoothness in complex cons...
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