TITLE:
Three-Dimensional Complete Coverage Path Planning for UAVs Using an Improved Multi-Objective Dung Beetle Optimization Algorithm
AUTHORS:
Ziye Shi, Wenxing Wu, Liqin Tian
KEYWORDS:
UAV, Dung Beetle Optimization Algorithm, Latin Hypercube Sampling, Perturbation Strategy, Coverage Path
JOURNAL NAME:
Journal of Computer and Communications,
Vol.14 No.7,
July
28,
2026
ABSTRACT: To address coverage blind spots and path redundancy in UAV complete coverage path planning in complex three-dimensional environments, this paper constructs a multi-objective optimization model. The model targets coverage completeness, the number of nodes, and path length. We also address insufficient population diversity, an imbalance between exploration and exploitation, and a tendency to fall into local optima during optimization. To do so, we propose an improved multi-strategy multi-objective dung beetle optimization algorithm. This algorithm is termed the Improved Multi-Objective Dung Beetle Optimizer (IMODBO). It enhances population diversity using Latin hypercube sampling for initialization. It introduces dynamic parameters to balance exploration and exploitation. It also designs a sine-cosine perturbation strategy to improve local search capability. Results on the CEC2020 benchmark suite verify that the proposed algorithm delivers better convergence and solution distribution performance for most test functions, with only a few individual metrics of special Pareto front problems slightly inferior to those of partial comparison algorithms. Simulation on three-dimensional terrains shows that, with the same number of nodes, our algorithm increases the coverage rate from 74.6% to 87.2%. It also reduces the flight path length, thereby effectively mitigating the coverage blind spot problem. This work provides technical support for complete 3D coverage operations by UAVs.