TL;DR
Comparative experiments demonstrate that MsESO exhibits higher robustness and superiority over CMA-ES, MadDE, LSHADE-SPACMA, WOA, HHO, PPSO, MELGWO, HLOA, NRBO, ESO, and the original SO, and outperforms the comparative algorithms in UAV path planning problems, showcasing its significant potential in practical applications.
The improved algorithm is compared with five mainstream swarm intelligence algorithms on ten benchmark functions to verify its effectiveness and the simulation results of three-dimensional trajectory planning using the improved algorithm and other swarm intelligence algorithms are presented, which demonstrate the super...
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.
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...
To address the issues of slow convergence and susceptibility to local optima in traditional algorithms for UAV path planning in complex 3D environments, we propose a Differential Red-billed Magpie Optimizer (DRBMO). Initially, a piecewise chaotic mapping is employed to initialize the population, enhancing its diversity...
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.
The proposed SPO provides an effective alternative optimization tool for complex constrained engineering optimization tasks such as 3D UAV path planning and significantly outperforms 14 mainstream metaheuristic algorithms, including PSO, DE, SHADE, and DBO, on most test functions.
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