This study proposes a metaheuristic-based route optimization framework that balances earthworks in highway design by referencing a constant-slope “zero polygon.” The aim is to distribute excavation and embankment volumes evenly between fixed endpoints. The positions of Point of Intersection nodes and curve radii are optimized using the Social Spider Optimization (SSO) algorithm, guided by an objective function that minimizes the weighted signed distances between the route and the zero polygon while enforcing geometric constraints such as minimum curve radius, tangent length, and alignment continuity through penalty terms. Applications on two terrains with varying slopes demonstrate that the calibrated model substantially improves the cut–fill balance compared with both manual and pre-calibration solutions: the absolute difference between excavation and embankment volumes decreased from thousands to hundreds of cubic meters, and the cut–fill ratio fell below 0.1 in representative cases. Coupling a swarm-intelligence search with an interpretable geometric reference line, the framework offers a reproducible decision-support tool for preliminary corridor design. It enhances transparency and reduces reliance on trial-and-error practice. It targets preliminary, open-terrain corridor selection under a constant longitudinal slope without vertical curves, and does not yet incorporate land-use, environmental, or geotechnical constraints.
This paper proposes a hybrid optimization algorithm that fuses multiple methods to address the weak global exploration ability, frequent local optima, and poor engineering adaptability in masonry layout optimization of building infill walls. The method builds a multilayer cooperative framework. It first uses the Genetic Algorithm to create a diverse population. It then applies Simulated Annealing to perform probabilistic jumping optimization. After that, it introduces sparse A search to verify topological feasibility. It finally relies on a cooperative mechanism of Adaptive Whale Optimization and iterative local search to explore the solution space in depth. Experiments on the simultaneous localization and mapping–building information modeling coupled dataset and the building information modeling component multimodal dataset show that the algorithm reaches a standard block utilization rate of 98.76 percent. It also keeps the cutting loss rate as low as 2.79%and achieves a peak stagger-joint compliance rate of 97.11%. In irregular wall scenarios, it reduces cost by up to 33.87%. The results show that this algorithm improves the optimization quality and engineering applicability of masonry layout and provides reliable technical support for precise construction and efficient material use of building infill walls.
Yuanzhe Chen, Feifei Chen· Research on Engineering Stru...· 0 citations
Designing transportation routes over complex terrain requires balancing distance with constructability constraints such as slope, elevation, and obstacle avoidance. We propose a hybrid framework that combines Ant Colony Optimization (ACO) with Genetic Programming (GP) on a high-resolution geospatial mesh represented as an 8-neighbor graph enriched with terrain features. GP is used to evolve an edge-cost function via a penalty-driven fitness over multiple start-goal pairs, capturing trade-offs between competing objectives. The learned cost is then exploited by a parallel Min-Max Ant System to efficiently generate feasible routes between user-defined locations. Results show that the proposed method produces computationally efficient routes that reduce steep segments and avoid undesirable areas while maintaining limited detours.
Asia Panizza, Davide Colosimo, Filippo Marcon et al.· Proceedings of the Genetic a...· 0 citations
This study addresses the safety and efficiency challenges of manual inspection in complex open-pit mining environments, where terrain steepness, limited coverage, and personnel exposure to hazards render conventional methods inadequate. We propose an integrated UAV inspection framework that combines 3D environmental modeling with a hierarchical optimization paradigm. The framework operates in three sequential stages. First, a high-fidelity 3D terrain model is constructed from point cloud data via skeletal feature extraction, which reduces computational complexity while preserving topographic structure. Second, an upper-layer Traveling Salesman Problem (TSP) solver determines the optimal inspection sequence across mandatory points (loading sites, dump sites, and crushing stations). Third, a lower-layer Chaotic Adaptive Population-based Grey Wolf Optimizer (CAP-GWO) refines the 3D path between consecutive TSP-ordered points, augmented by B-spline smoothing to ensure kinematic feasibility. Key inputs include: (i) raw LiDAR point cloud data of the mining site, (ii) facility coordinates and operational constraints (safety margins, maximum pitch angle, minimum turn radius), and (iii) UAV kinematic parameters. Outputs comprise a smooth, collision-free 3D trajectory with verified constraint satisfaction. Comparative experiments against eight metaheuristic algorithms (PSO, GA, ACO, BA, COA, GWO, SRA, SFOA) demonstrate that the proposed method reduces total path length by 15–20% on synthetic benchmark scenarios while maintaining zero constraint violations. Statistical validation via the Sign Test confirms the significance of these improvements (p < 0.05) across repeated independent trials. The framework is further validated on measured airborne LiDAR data of the Bingham Canyon open-pit copper mine (Utah, USA; USGS 3D Elevation Program), one of the largest operating open-pit mines in the world: on this real terrain, CAP-GWO achieves the best performance among the GWO-family algorithms, with a statistically significant 12.5% improvement over SRA (Wilcoxon p < 0.001) and 24% lower variance than the standard GWO, and all 210 experimental runs produce collision-free trajectories. Notably, the proposed hierarchical optimization framework achieves structural symmetry between the upper-layer sequencing task and the lower-layer path refinement task. This symmetric decomposition significantly reduces computational complexity while preserving solution quality, aligning with the principles of symmetry in engineering optimization. The framework offers a practical solution for autonomous, adaptive inspection scheduling in dynamic mining environments.