A comprehensive analysis of multi-strategic RIME algorithm for UAV path planning in varied terrains

Abstract

Optimizing industrial information integration is fundamental to harnessing the potential of Industry 4.0, driving data-informed decisions that enhance operational efficiency, reduce costs, and improve competitiveness in modern industrial environments. Effective unmanned aerial vehicle (UAV) path planning is crucial within this optimization framework, supporting timely and reliable data collection and transmission for smarter decision-making. This study proposes an enhanced RIME (IRIME) algorithm for three-dimensional UAV path planning in complex urban environments, formulated as a multiconstraint optimization problem aimed at discovering optimal flight paths in intricate configuration spaces. IRIME integrates three strategic innovations into the RIME algorithm: a frost crystal diffusion mechanism for improved initial population diversity, a high-altitude condensation strategy to enhance global exploration, and a lattice weaving strategy to avoid premature convergence. Evaluated on the CEC2017 test set and six realistic urban scenarios, IRIME achieves an 86.21 % win rate across 100 functions. In scenarios 4–6, IRIME uniquely identifies the globally optimal paths, outperforming other algorithms that are limited to locally optimal solutions. We believe these findings demonstrate IRIME's capacity to address complex path-planning challenges, laying a robust foundation for its future application to broader industrial optimization tasks.