An investigation of a differential red-billed blue magpie optimization algorithm for UAV three-dimensional path planning
TL;DR
Comparative experiments conducted across four 3D scenarios demonstrate that DRBMO consistently outperforms other algorithms in terms of path cost, and demonstrates the superior robustness and optimization capability of DRBMO.
Abstract
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. A progressive dynamic step size optimization formula is then applied to improve both global search and local exploitation capabilities. Additionally, a differential evolution operator is introduced to enhance local search efficiency. An improved dynamic weight factor is incorporated to prevent the algorithm from falling into local optima. Comparative experiments conducted across four 3D scenarios of varying complexity demonstrate that DRBMO consistently outperforms other algorithms in terms of path cost. In the most complex scenario, DRBMO outperformed the improved Red-billed Blue Magpie Optimizer, the original Red-billed Blue Magpie Optimizer, the improved Grey Wolf Optimizer, the Dung Beetle Optimizer, and the improved Particle Swarm Optimization algorithm. The performance improvements were 9.21%, 16.97%, 17.52%, 18.69%, and 19.18%, respectively. These results demonstrate the superior robustness and optimization capability of DRBMO.